diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS
index 9089a370f..1c1407381 100644
--- a/.github/CODEOWNERS
+++ b/.github/CODEOWNERS
@@ -3,5 +3,5 @@
 # least one codeowner. However, all Qiskit team members can (and should!) review the PRs.
 
 # Global rule, unless specialized by a later one
-* @stefan-woerner @manoelmarques @woodsp-ibm @t-imamichi
+* @stefan-woerner @woodsp-ibm @t-imamichi
 
diff --git a/.github/ISSUE_TEMPLATE/ENHANCEMENT_REQUEST.yaml b/.github/ISSUE_TEMPLATE/ENHANCEMENT_REQUEST.yaml
deleted file mode 100644
index c433439ef..000000000
--- a/.github/ISSUE_TEMPLATE/ENHANCEMENT_REQUEST.yaml
+++ /dev/null
@@ -1,14 +0,0 @@
-name: 💅 Enhancement request
-description: Suggest an improvement for this project 🆒!
-labels: ["type: enhancement"]
-
-body:
-  - type: markdown
-    attributes:
-      value: Please make sure to browse the opened and closed issues to make sure that this idea has not previously been discussed.
-
-  - type: textarea
-    attributes:
-      label: What is the expected enhancement?
-    validations:
-      required: true
diff --git a/.github/actions/install-main-dependencies/action.yml b/.github/actions/install-main-dependencies/action.yml
index b2014ecce..ba6f6b8ae 100644
--- a/.github/actions/install-main-dependencies/action.yml
+++ b/.github/actions/install-main-dependencies/action.yml
@@ -1,6 +1,6 @@
 # This code is part of Qiskit.
 #
-# (C) Copyright IBM 2021, 2022.
+# (C) Copyright IBM 2021, 2023.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -25,7 +25,6 @@ runs:
     - name: Get main last commit ids
       run: |
         echo "TERRA_HASH=$(git ls-remote --heads https://github.com/Qiskit/qiskit-terra.git refs/heads/main  | awk '{print $1}')" >> $GITHUB_ENV
-        echo "AER_HASH=$(git ls-remote --heads https://github.com/Qiskit/qiskit-aer.git refs/heads/main  | awk '{print $1}')" >> $GITHUB_ENV
       shell: bash
     - name: Terra Cache
       env:
@@ -35,14 +34,6 @@ runs:
       with:
         path: terra-cache
         key: terra-cache-${{ inputs.os }}-${{ inputs.python-version }}-${{ env.TERRA_HASH }}-${{ env.CACHE_VERSION }}
-    - name: Aer Cache
-      env:
-        CACHE_VERSION: v1
-      id: aer-cache
-      uses: actions/cache@v3
-      with:
-        path: aer-cache
-        key: aer-cache-${{ inputs.os }}-${{ inputs.python-version }}-${{ env.AER_HASH }}-${{ env.CACHE_VERSION }}
     - name: Install Terra from Main
       env:
         MACOSX_DEPLOYMENT_TARGET: 10.15
@@ -81,63 +72,12 @@ runs:
           pip uninstall -y setuptools_rust
         fi
       shell: bash
-    - name: Install Aer from Main
-      env:
-        MACOSX_DEPLOYMENT_TARGET: 10.16
+    - name: Install stable Aer
       run: |
-        echo 'Install Aer from Main'
+        echo 'Install stable Aer'
         if [ "${{ inputs.os }}" == "windows-2019" ]; then
           source "$CONDA/etc/profile.d/conda.sh"
           conda activate scsenv
         fi
-        if [ "${{ inputs.os }}" == "ubuntu-latest" ]; then
-          export DISABLE_CONAN=1
-          sudo apt-get -y install nlohmann-json3-dev
-          sudo apt-get -y install libspdlog-dev
-          sudo apt-get -y install libmuparserx-dev
-        fi
-        git clone --depth 1 --branch main https://github.com/Qiskit/qiskit-aer.git /tmp/qiskit-aer
-        BASE_DIR=aer-cache
-        build_from_main=true
-        cache_hit=${{ steps.aer-cache.outputs.cache-hit }}
-        echo "cache hit: ${cache_hit}"
-        if [ "$cache_hit" == "true" ]; then
-          pip_result=0
-          pushd "${BASE_DIR}"
-          python -m pip install *.whl && pip_result=$? || pip_result=$?
-          popd
-          if [ $pip_result == 0 ]; then
-            echo 'Verifying cached Aer with tools/verify_wheels.py ...'
-            verify_result=0
-            pushd /tmp/qiskit-aer
-            python tools/verify_wheels.py && verify_result=$? || verify_result=$?
-            popd
-            if [ $verify_result == 0 ]; then
-              echo 'Cached Aer passed verification.'
-              build_from_main=false
-            else
-              echo 'Cached Aer failed verification.'
-            fi
-          fi
-        else
-          mkdir -p ${BASE_DIR}
-        fi
-        if [ "$build_from_main" == "true" ]; then
-          echo 'Create wheel file from main'
-          pip install -U wheel
-          pushd /tmp/qiskit-aer
-          if [ "${{ inputs.os }}" == "windows-2019" ]; then
-            python setup.py bdist_wheel -- -G 'Visual Studio 16 2019'
-          elif  [ "${{ inputs.os }}" == "macos-latest" ]; then
-            pip install -U -c constraints.txt -r requirements-dev.txt
-            python setup.py bdist_wheel --plat-name macosx-10.16-x86_64
-          else
-            python setup.py bdist_wheel
-          fi
-          popd
-          cp -rf /tmp/qiskit-aer/dist/*.whl "${BASE_DIR}"
-          pushd "${BASE_DIR}"
-          python -m pip install *.whl
-          popd
-        fi
+        pip install -U qiskit-aer
       shell: bash
diff --git a/.github/workflows/deploy-docs.yml b/.github/workflows/deploy-docs.yml
index 64d6f0851..85fbdeffb 100644
--- a/.github/workflows/deploy-docs.yml
+++ b/.github/workflows/deploy-docs.yml
@@ -21,7 +21,7 @@ concurrency:
 
 jobs:
   docs_publish:
-    if: ${{ startsWith(github.ref, 'refs/heads/stable') && contains('["manoelmarques","mtreinish","stefan-woerner","woodsp-ibm"]', github.actor) }}
+    if: ${{ startsWith(github.ref, 'refs/heads/stable') && contains('["t-imamichi","mtreinish","stefan-woerner","woodsp-ibm"]', github.actor) }}
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
@@ -55,7 +55,7 @@ jobs:
           tools/deploy_documentation.sh
         shell: bash
   deploy-translatable-strings:
-    if: ${{ startsWith(github.ref, 'refs/heads/stable') && contains('["manoelmarques","mtreinish","stefan-woerner","woodsp-ibm"]', github.actor) }}
+    if: ${{ startsWith(github.ref, 'refs/heads/stable') && contains('["t-imamichi","mtreinish","stefan-woerner","woodsp-ibm"]', github.actor) }}
     runs-on: ubuntu-latest
     strategy:
       matrix:
diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml
index 8614fd4f4..d5a534b2f 100644
--- a/.github/workflows/main.yml
+++ b/.github/workflows/main.yml
@@ -1,6 +1,6 @@
 # This code is part of Qiskit.
 #
-# (C) Copyright IBM 2021, 2022.
+# (C) Copyright IBM 2021, 2023.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -113,12 +113,12 @@ jobs:
       fail-fast: false
       matrix:
         os: [ubuntu-latest]
-        python-version: [3.7, 3.8, 3.9, '3.10.6']
+        python-version: [3.7, 3.8, 3.9, '3.10', 3.11]
         include:
           - os: macos-latest
             python-version: 3.8
           - os: macos-latest
-            python-version: '3.10.6'
+            python-version: '3.10'
           - os: windows-2019
             python-version: 3.8
           - os: windows-2019
@@ -136,7 +136,7 @@ jobs:
       - name: install Windows dependencies
         run: |
           source "$CONDA/etc/profile.d/conda.sh"
-          conda create -n scsenv python=${{ matrix.python-version }}
+          conda create -y -n scsenv python=${{ matrix.python-version }} -c conda-forge
           conda activate scsenv
           conda install -y scs lapack cvxpy -c conda-forge
         if: ${{ matrix.os == 'windows-2019' }}
@@ -166,15 +166,6 @@ jobs:
           make mypy
         if: ${{ !cancelled() }}
         shell: bash
-      - name: Run lint latest version
-        run: |
-          if [ "${{ matrix.os }}" == "windows-2019" ]; then
-            source "$CONDA/etc/profile.d/conda.sh"
-            conda activate scsenv
-          fi
-          pip install -U -r requirements-dev.txt
-          make lint
-        shell: bash
       - name: Optimization Unit Tests under Python ${{ matrix.python-version }}
         uses: ./.github/actions/run-tests
         with:
@@ -224,6 +215,7 @@ jobs:
   Tutorials:
     runs-on: ${{ matrix.os }}
     strategy:
+      fail-fast: false
       matrix:
         os: [ubuntu-latest]
         python-version: [3.7, 3.8]
@@ -312,7 +304,7 @@ jobs:
           python-version: ${{ matrix.python-version }}
       - uses: actions/download-artifact@v3
         with:
-          name:  ubuntu-latest-3.7
+          name: ubuntu-latest-3.7
           path: /tmp/o37
       - uses: actions/download-artifact@v3
         with:
@@ -324,15 +316,19 @@ jobs:
           path: /tmp/o39
       - uses: actions/download-artifact@v3
         with:
-          name: ubuntu-latest-3.10.6
+          name: ubuntu-latest-3.10
           path: /tmp/o310
+      - uses: actions/download-artifact@v3
+        with:
+          name: ubuntu-latest-3.11
+          path: /tmp/o311
       - uses: actions/download-artifact@v3
         with:
           name: macos-latest-3.8
           path: /tmp/m38
       - uses: actions/download-artifact@v3
         with:
-          name: macos-latest-3.10.6
+          name: macos-latest-3.10
           path: /tmp/m310
       - uses: actions/download-artifact@v3
         with:
@@ -347,7 +343,7 @@ jobs:
         shell: bash
       - name: Combined Deprecation Messages
         run: |
-          sort -f -u /tmp/o37/opt.dep /tmp/o38/opt.dep /tmp/o39/opt.dep /tmp/o310/opt.dep /tmp/m38/opt.dep /tmp/m310/opt.dep /tmp/w38/opt.dep /tmp/w310/opt.dep || true
+          sort -f -u /tmp/o37/opt.dep /tmp/o38/opt.dep /tmp/o39/opt.dep /tmp/o310/opt.dep /tmp/o311/opt.dep /tmp/m38/opt.dep /tmp/m310/opt.dep /tmp/w38/opt.dep /tmp/w310/opt.dep || true
         shell: bash
       - name: Coverage combine
         run: coverage3 combine /tmp/o37/opt.dat
diff --git a/.mergify.yml b/.mergify.yml
index 40210f0a0..93ea659ab 100644
--- a/.mergify.yml
+++ b/.mergify.yml
@@ -20,4 +20,4 @@ pull_request_rules:
     actions:
       backport:
         branches:
-          - stable/0.4
+          - stable/0.5
diff --git a/.pylintdict b/.pylintdict
index 4a56f4df4..23743a822 100644
--- a/.pylintdict
+++ b/.pylintdict
@@ -11,6 +11,7 @@ args
 arxiv
 autosummary
 backend
+backends
 barkoutsos
 benchmarking
 bitstring
@@ -50,6 +51,7 @@ eckstein
 egger
 eigen
 eigensolver
+eigensolvers
 eigenstate
 entangler
 enum
@@ -81,6 +83,7 @@ hamiltonian
 hamiltonians
 hastings
 hoyer
+ibm
 ide
 imode
 init
@@ -127,7 +130,9 @@ nosignatures
 np
 num
 numpy
+numpyminimumeigensolver
 october
+opflow
 optimality
 optimizationresult
 optimizationresultstatus
@@ -219,6 +224,7 @@ upperbound
 variational
 vartype
 vqe
+vqeresult
 writelines
 xixj
 wavefunction
diff --git a/.pylintrc b/.pylintrc
index 82ef8f4ed..00c2fd1af 100644
--- a/.pylintrc
+++ b/.pylintrc
@@ -58,8 +58,7 @@ confidence=
 # --enable=similarities". If you want to run only the classes checker, but have
 # no Warning level messages displayed, use"--disable=all --enable=classes
 # --disable=W"
-disable=no-self-use,        # disabled as it is too verbose
-    fixme,              # disabled as TODOs would show up as warnings
+disable=fixme,              # disabled as TODOs would show up as warnings
     protected-access,   # disabled as we don't follow the public vs private
                         # convention strictly
     duplicate-code,     # disabled as it is too verbose
@@ -72,9 +71,6 @@ disable=no-self-use,        # disabled as it is too verbose
     no-else-return,     # relax "elif" after a clause with a return
     docstring-first-line-empty, # relax docstring style
     import-outside-toplevel,
-    bad-continuation, bad-whitespace, # differences of opinion with black
-
-
 
 
 [REPORTS]
@@ -84,12 +80,6 @@ disable=no-self-use,        # disabled as it is too verbose
 # mypackage.mymodule.MyReporterClass.
 output-format=text
 
-# Put messages in a separate file for each module / package specified on the
-# command line instead of printing them on stdout. Reports (if any) will be
-# written in a file name "pylint_global.[txt|html]". This option is deprecated
-# and it will be removed in Pylint 2.0.
-files-output=no
-
 # Tells whether to display a full report or only the messages
 reports=yes
 
@@ -138,63 +128,33 @@ property-classes=abc.abstractproperty
 # Regular expression matching correct module names
 module-rgx=(([a-z_][a-z0-9_]*)|([A-Z][a-zA-Z0-9]+))$
 
-# Naming hint for module names
-module-name-hint=(([a-z_][a-z0-9_]*)|([A-Z][a-zA-Z0-9]+))$
-
 # Regular expression matching correct constant names
 const-rgx=(([A-Z_][A-Z0-9_]*)|(__.*__))$
 
-# Naming hint for constant names
-const-name-hint=(([A-Z_][A-Z0-9_]*)|(__.*__))$
-
 # Regular expression matching correct class names
 class-rgx=[A-Z_][a-zA-Z0-9]+$
 
-# Naming hint for class names
-class-name-hint=[A-Z_][a-zA-Z0-9]+$
-
 # Regular expression matching correct function names
 function-rgx=[a-z_][a-z0-9_]{2,30}$
 
-# Naming hint for function names
-function-name-hint=[a-z_][a-z0-9_]{2,30}$
-
 # Regular expression matching correct method names
 method-rgx=(([a-z_][a-z0-9_]{2,49})|(assert[A-Z][a-zA-Z0-9]{2,43})|(test_[_a-zA-Z0-9]{2,}))$
 
-# Naming hint for method names
-method-name-hint=[a-z_][a-z0-9_]{2,30}$ or camelCase `assert*` in tests.
-
 # Regular expression matching correct attribute names
 attr-rgx=[a-z_][a-z0-9_]{2,30}$
 
-# Naming hint for attribute names
-attr-name-hint=[a-z_][a-z0-9_]{2,30}$
-
 # Regular expression matching correct argument names
 argument-rgx=[a-z_][a-z0-9_]{2,30}|ax|dt$
 
-# Naming hint for argument names
-argument-name-hint=[a-z_][a-z0-9_]{2,30}$
-
 # Regular expression matching correct variable names
 variable-rgx=[a-z_][a-z0-9_]{2,30}$
 
-# Naming hint for variable names
-variable-name-hint=[a-z_][a-z0-9_]{2,30}$
-
 # Regular expression matching correct class attribute names
 class-attribute-rgx=([A-Za-z_][A-Za-z0-9_]{2,30}|(__.*__))$
 
-# Naming hint for class attribute names
-class-attribute-name-hint=([A-Za-z_][A-Za-z0-9_]{2,30}|(__.*__))$
-
 # Regular expression matching correct inline iteration names
 inlinevar-rgx=[A-Za-z_][A-Za-z0-9_]*$
 
-# Naming hint for inline iteration names
-inlinevar-name-hint=[A-Za-z_][A-Za-z0-9_]*$
-
 # Regular expression which should only match function or class names that do
 # not require a docstring.
 no-docstring-rgx=^_
@@ -222,12 +182,6 @@ ignore-long-lines=^\s*(# )?<?https?://\S+>?$
 # else.
 single-line-if-stmt=no
 
-# List of optional constructs for which whitespace checking is disabled. `dict-
-# separator` is used to allow tabulation in dicts, etc.: {1  : 1,\n222: 2}.
-# `trailing-comma` allows a space between comma and closing bracket: (a, ).
-# `empty-line` allows space-only lines.
-no-space-check=trailing-comma,dict-separator
-
 # Maximum number of lines in a module
 max-module-lines=1000
 
@@ -424,4 +378,4 @@ analyse-fallback-blocks=no
 
 # Exceptions that will emit a warning when being caught. Defaults to
 # "Exception"
-overgeneral-exceptions=Exception
+overgeneral-exceptions=builtins.Exception
diff --git a/Makefile b/Makefile
index 81e88d8a3..a0ebfbfd8 100644
--- a/Makefile
+++ b/Makefile
@@ -1,6 +1,6 @@
 # This code is part of Qiskit.
 #
-# (C) Copyright IBM 2021, 2022.
+# (C) Copyright IBM 2021, 2023.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -48,10 +48,10 @@ mypy:
 	mypy qiskit_optimization test tools
 
 style:
-	black --check qiskit_optimization test tools docs
+	black --check qiskit_optimization test tools docs setup.py
 
 black:
-	black qiskit_optimization test tools docs
+	black qiskit_optimization test tools docs setup.py
 
 test:
 	python -m unittest discover -v test
diff --git a/README.md b/README.md
index 5e55d38ce..34c237ba3 100644
--- a/README.md
+++ b/README.md
@@ -68,9 +68,9 @@ from docplex.mp.model import Model
 from qiskit_optimization.algorithms import MinimumEigenOptimizer
 from qiskit_optimization.translators import from_docplex_mp
 
-from qiskit.utils import algorithm_globals, QuantumInstance
-from qiskit import BasicAer
-from qiskit.algorithms import QAOA
+from qiskit.utils import algorithm_globals
+from qiskit.primitives import Sampler
+from qiskit.algorithms.minimum_eigensolvers import QAOA
 from qiskit.algorithms.optimizers import SPSA
 
 # Generate a graph of 4 nodes
@@ -97,9 +97,8 @@ seed = 1234
 algorithm_globals.random_seed = seed
 
 spsa = SPSA(maxiter=250)
-backend = BasicAer.get_backend('qasm_simulator')
-q_i = QuantumInstance(backend=backend, seed_simulator=seed, seed_transpiler=seed)
-qaoa = QAOA(optimizer=spsa, reps=5, quantum_instance=q_i)
+sampler = Sampler()
+qaoa = QAOA(sampler=sampler, optimizer=spsa, reps=5)
 algorithm = MinimumEigenOptimizer(qaoa)
 result = algorithm.solve(problem)
 print(result.prettyprint())  # prints solution, x=[1, 0, 1, 0], the cost, fval=4
@@ -121,7 +120,7 @@ This project adheres to Qiskit's [code of conduct](https://github.com/Qiskit/qis
 By participating, you are expected to uphold this code.
 
 We use [GitHub issues](https://github.com/Qiskit/qiskit-optimization/issues) for tracking requests and bugs. Please
-[join the Qiskit Slack community](https://ibm.co/joinqiskitslack)
+[join the Qiskit Slack community](https://qisk.it/join-slack)
 and for discussion and simple questions.
 For questions that are more suited for a forum, we use the **Qiskit** tag in [Stack Overflow](https://stackoverflow.com/questions/tagged/qiskit).
 
diff --git a/constraints.txt b/constraints.txt
index 8448ac45c..bba3992ca 100644
--- a/constraints.txt
+++ b/constraints.txt
@@ -1,3 +1 @@
-astroid==2.5.6
-pylint==2.8.3
 numpy>=1.20.0
diff --git a/docs/_templates/layout.html b/docs/_templates/layout.html
deleted file mode 100644
index 242acdf0e..000000000
--- a/docs/_templates/layout.html
+++ /dev/null
@@ -1,401 +0,0 @@
-{# TEMPLATE VAR SETTINGS #}
-{%- set url_root = pathto('', 1) %}
-{%- if url_root == '#' %}{% set url_root = '' %}{% endif %}
-{%- if not embedded and docstitle %}
-  {%- set titlesuffix = " &mdash; "|safe + docstitle|e %}
-{%- else %}
-  {%- set titlesuffix = "" %}
-{%- endif %}
-{%- set lang_attr = 'en' if language == None else (language | replace('_', '-')) %}
-{% import 'theme_variables.jinja' as theme_variables %}
-
-<!DOCTYPE html>
-<!--[if IE 8]><html class="no-js lt-ie9" lang="{{ lang_attr }}" > <![endif]-->
-<!--[if gt IE 8]><!--> <html class="no-js" lang="{{ lang_attr }}" > <!--<![endif]-->
-<head>
-  <meta charset="utf-8">
-  {{ metatags }}
-  <meta name="viewport" content="width=device-width, initial-scale=1.0">
-  {% block htmltitle %}
-  <title>{{ title|striptags|e }}{{ titlesuffix }}</title>
-  {% endblock %}
-
-  {# FAVICON #}
-  {% if favicon %}
-    <link rel="shortcut icon" href="{{ pathto('_static/' + favicon, 1) }}"/>
-  {% endif %}
-  {# CANONICAL URL #}
-  {% if theme_canonical_url %}
-    <link rel="canonical" href="{{ theme_canonical_url }}{{ pagename }}.html"/>
-  {% endif %}
-
-  {# CSS #}
-
-  {# OPENSEARCH #}
-  {% if not embedded %}
-    {% if use_opensearch %}
-      <link rel="search" type="application/opensearchdescription+xml"
-            title="{% trans docstitle=docstitle|e %}Search within {{ docstitle }}{% endtrans %}"
-            href="{{ pathto('_static/opensearch.xml', 1) }}"/>
-    {% endif %}
-
-  {% endif %}
-
-  <link rel="stylesheet" href="{{ pathto('_static/' + style, 1) }}" type="text/css" />
-  <!-- <link rel="stylesheet" href="{{ pathto('_static/pygments.css', 1) }}" type="text/css" /> -->
-  {%- for css in css_files %}
-    {%- if css|attr("rel") %}
-  <link rel="{{ css.rel }}" href="{{ pathto(css.filename, 1) }}" type="text/css"{% if css.title is not none %} title="{{ css.title }}"{% endif %} />
-    {%- else %}
-  <link rel="stylesheet" href="{{ pathto(css, 1) }}" type="text/css" />
-    {%- endif %}
-  {%- endfor %}
-  {%- for cssfile in extra_css_files %}
-    <link rel="stylesheet" href="{{ pathto(cssfile, 1) }}" type="text/css" />
-  {%- endfor %}
-
-  {%- block linktags %}
-    {%- if hasdoc('about') %}
-    <link rel="author" title="{{ _('About these documents') }}" href="{{ pathto('about') }}" />
-    {%- endif %}
-    {%- if hasdoc('genindex') %}
-    <link rel="index" title="{{ _('Index') }}" href="{{ pathto('genindex') }}" />
-    {%- endif %}
-    {%- if hasdoc('search') %}
-    <link rel="search" title="{{ _('Search') }}" href="{{ pathto('search') }}" />
-    {%- endif %}
-    {%- if hasdoc('copyright') %}
-    <link rel="copyright" title="{{ _('Copyright') }}" href="{{ pathto('copyright') }}" />
-    {%- endif %}
-    {%- if next %}
-    <link rel="next" title="{{ next.title|striptags|e }}" href="{{ next.link|e }}" />
-    {%- endif %}
-    {%- if prev %}
-    <link rel="prev" title="{{ prev.title|striptags|e }}" href="{{ prev.link|e }}" />
-    {%- endif %}
-  {%- endblock %}
-  {%- block extrahead %} {% endblock %}
-
-  {# Keep modernizr in head - http://modernizr.com/docs/#installing #}
-  <script src="{{ pathto('_static/js/modernizr.min.js', 1) }}"></script>
-
-</head>
-
-<div class="container-fluid header-holder tutorials-header" id="header-holder">
-  <div class="container">
-    <div class="header-container">
-      <a class="header-logo" href="{{ theme_variables.external_urls['home'] }}" aria-label="PyTorch"></a>
-
-      <div class="main-menu">
-        <ul>
-          <li {%- if pagename == 'getting_started' %} class="active"{% endif %}>
-            <a href="{{ pathto('getting_started') }}">Getting started</a>
-          </li>
-
-          <li {%- if pagename == 'tutorials' %} class="active"{% endif %}>
-            <a href="{{ theme_variables.external_urls['tutorials'] }}" target="_blank">Tutorials</a>
-          </li>
-
-          <li>
-            <a href="{{ theme_variables.external_urls['partners'] }}" target="_blank">Partners</a>
-          </li>
-
-          <li>
-            <div id="resourcesDropdownButton" data-toggle="resources-dropdown" class="resources-dropdown">
-              <a class="resource-option with-down-orange-arrow">
-                Applications
-              </a>
-              <div class="resources-dropdown-menu">
-                <a class="nav-dropdown-item" href="{{ theme_variables.external_urls['ml'] }}">
-                  <span class="dropdown-title">Machine learning</span>
-                  <p>QSVM, VQC (Variational Quantum Classifier), and QGAN (Quantum Generative
-                    Adversarial Network) algorithms.</p>
-                </a>
-                <a class="nav-dropdown-item" href="{{ theme_variables.external_urls['nature'] }}">
-                  <span class="dropdown-title">Nature</span>
-                  <p>Quantum applications in chemistry, physics, and biology.</p>
-                </a>
-                <a class="nav-dropdown-item" href="{{ theme_variables.external_urls['finance'] }}">
-                  <span class="dropdown-title">Finance</span>
-                  <p>Uncertainty components for stock/securities problems, Ising translators for
-                    portfolio optimizations and data providers to source real or random data.</p>
-                </a>
-                <a class="nav-dropdown-item" href="{{ theme_variables.external_urls['optim'] }}">
-                  <span class="dropdown-title">Optimization</span>
-                  <p>High-level optimization problems that are ready to
-                    run on simulators and real quantum devices</p>
-                </a>
-              </div>
-          </li>
-          <li>
-              <a href="{{ theme_variables.external_urls['experiments'] }}" target="_blank">Experiments</a>
-          </li>
-          <li>
-            <div id="resourcesDropdownButton" data-toggle="resources-dropdown" class="resources-dropdown">
-              <a class="resource-option with-down-orange-arrow">
-                Resources
-              </a>
-              <div class="resources-dropdown-menu">
-                <a class="doc-dropdown-option nav-dropdown-item" href="{{ theme_variables.external_urls['slack'] }}">
-                  <span class="dropdown-title">Slack support</span>
-                  <p></p>
-                </a>
-                <a class="doc-dropdown-option nav-dropdown-item" href="{{ theme_variables.external_urls['textbook'] }}">
-                  <span class="dropdown-title">Qiskit Textbook</span>
-                  <p></p>
-                </a>
-                <a class="doc-dropdown-option nav-dropdown-item" href="{{ theme_variables.external_urls['events'] }}">
-                  <span class="dropdown-title">Qiskit events</span>
-                  <p></p>
-                </a>
-            </div>
-          </li>
-          <li>
-            <a href="{{ theme_variables.external_urls['github'] }}" target="_blank">Github</a>
-          </li>
-        </ul>
-      </div>
-
-      <a class="main-menu-open-button" href="#" data-behavior="open-mobile-menu"></a>
-    </div>
-
-  </div>
-</div>
-
-
-<body class="pytorch-body">
-
-  {% block extrabody %} {% endblock %}
-
-    {# SIDE NAV, TOGGLES ON MOBILE #}
-
-    {% include "versions.html" %}
-
-    <div class="table-of-contents-link-wrapper">
-      <span>Table of Contents</span>
-      <a href="#" class="toggle-table-of-contents" data-behavior="toggle-table-of-contents"></a>
-    </div>
-
-    <nav data-toggle="wy-nav-shift" class="pytorch-left-menu" id="pytorch-left-menu">
-      <div class="pytorch-side-scroll">
-        <div class="pytorch-menu pytorch-menu-vertical" data-spy="affix" role="navigation" aria-label="main navigation">
-          <div class="pytorch-left-menu-search">
-            {% block sidebartitle %}
-
-            {% if theme_display_version %}
-              {%- set nav_version = version %}
-              {% if READTHEDOCS and current_version %}
-                {%- set nav_version = current_version %}
-              {% endif %}
-              {% if nav_version %}
-                <div class="version">
-                  {{ nav_version }}
-                </div>
-              {% endif %}
-            {% endif %}
-
-            {% include "searchbox.html" %}
-
-            {% endblock %}
-          </div>
-
-          {% block menu %}
-            {#
-              The singlehtml builder doesn't handle this toctree call when the
-              toctree is empty. Skip building this for now.
-            #}
-            {% if 'singlehtml' not in builder %}
-              {% set global_toc = toctree(maxdepth=1,
-                                          collapse=theme_collapse_navigation|tobool,
-                                          includehidden=theme_includehidden|tobool,
-                                          titles_only=theme_titles_only|tobool) %}
-            {% endif %}
-            {% if global_toc %}
-              {{ global_toc }}
-            {% else %}
-              <!-- Local TOC -->
-              <div class="local-toc">{{ toc }}</div>
-            {% endif %}
-          {% endblock %}
-        </div>
-      </div>
-    </nav>
-
-    <div class="pytorch-container">
-      <div class="pytorch-page-level-bar" id="pytorch-page-level-bar">
-        <div class="pytorch-breadcrumbs-wrapper">
-          {% include "breadcrumbs.html" %}
-        </div>
-
-        <div class="pytorch-shortcuts-wrapper" id="pytorch-shortcuts-wrapper">
-          Shortcuts
-        </div>
-      </div>
-
-      <section data-toggle="wy-nav-shift" id="pytorch-content-wrap" class="pytorch-content-wrap">
-        <div class="pytorch-content-left">
-
-        {%- block content %}
-          {% if theme_style_external_links|tobool %}
-          <div class="rst-content style-external-links">
-          {% else %}
-          <div class="rst-content">
-          {% endif %}
-            <div role="main" class="main-content" itemscope="itemscope" itemtype="http://schema.org/Article">
-            {%- block document %}
-             <article itemprop="articleBody" id="pytorch-article" class="pytorch-article">
-              {% block body %}{% endblock %}
-             </article>
-             {% if self.comments()|trim %}
-             <div class="articleComments">
-              {% block comments %}{% endblock %}
-             </div>
-             {% endif%}
-            </div>
-            {%- endblock %}
-            {% include "footer.html" %}
-          </div>
-        {%- endblock %}
-        </div>
-
-        <div class="pytorch-content-right" id="pytorch-content-right">
-          <div class="pytorch-right-menu" id="pytorch-right-menu">
-            <div class="pytorch-side-scroll" id="pytorch-side-scroll-right">
-              {{ toc }}
-            </div>
-          </div>
-        </div>
-      </section>
-    </div>
-
-  {% if not embedded %}
-
-     {% if sphinx_version >= "1.8.0" %}
-       <script type="text/javascript" id="documentation_options" data-url_root="{{ pathto('', 1) }}" src="{{ pathto('_static/documentation_options.js', 1) }}"></script>
-       {%- for scriptfile in script_files %}
-         {{ js_tag(scriptfile) }}
-       {%- endfor %}
-     {% else %}
-       <script type="text/javascript">
-           var DOCUMENTATION_OPTIONS = {
-               URL_ROOT:'{{ url_root }}',
-               VERSION:'{{ release|e }}',
-               LANGUAGE:'{{ language }}',
-               COLLAPSE_INDEX:false,
-               FILE_SUFFIX:'{{ '' if no_search_suffix else file_suffix }}',
-               HAS_SOURCE:  {{ has_source|lower }},
-               SOURCELINK_SUFFIX: '{{ sourcelink_suffix }}'
-           };
-       </script>
-       {%- for scriptfile in script_files %}
-         <script type="text/javascript" src="{{ pathto(scriptfile, 1) }}"></script>
-       {%- endfor %}
-     {% endif %}
-
-  {% endif %}
-
-  <script type="text/javascript" src="{{ pathto('_static/js/vendor/popper.min.js', 1) }}"></script>
-  <script type="text/javascript" src="{{ pathto('_static/js/vendor/bootstrap.min.js', 1) }}"></script>
-  <script src="https://cdnjs.cloudflare.com/ajax/libs/list.js/1.5.0/list.min.js"></script>
-  <script type="text/javascript" src="{{ pathto('_static/js/theme.js', 1) }}"></script>
-
-  <script type="text/javascript">
-      jQuery(function () {
-          SphinxRtdTheme.Navigation.enable({{ 'true' if theme_sticky_navigation|tobool else 'false' }});
-      });
-  </script>
-
-{%- block footer %} {% endblock %}
-
-  <div>
-    <br>
-  </div>
-
-  <!-- Begin Mobile Menu -->
-
-  <div class="mobile-main-menu">
-    <div class="container-fluid">
-      <div class="container">
-        <div class="mobile-main-menu-header-container">
-          <a class="header-logo" href="{{ theme_variables.external_urls['home'] }}" aria-label="PyTorch"></a>
-          <a class="main-menu-close-button" href="#" data-behavior="close-mobile-menu"></a>
-        </div>
-      </div>
-    </div>
-
-    <div class="mobile-main-menu-links-container">
-      <div class="main-menu">
-        <ul>
-          <li>
-            <a href="{{ pathto('getting_started') }}">Getting Started</a>
-          </li>
-          <li>
-            <a href="{{ theme_variables.external_urls['tutorials'] }}" target="_blank">Tutorials</a>
-          </li>
-          <li>
-            <a href="{{ theme_variables.external_urls['partners'] }}">Partners</a>
-          </li>
-          <br>
-          <li class="resources-mobile-menu-title">
-            Applications
-          </li>
-          <ul class="resources-mobile-menu-items">
-            <li>
-              <a href="{{ theme_variables.external_urls['ml'] }}">Machine learning</a>
-            </li>
-
-            <li>
-              <a href="{{ theme_variables.external_urls['nature'] }}">Nature</a>
-            </li>
-
-            <li>
-              <a href="{{ theme_variables.external_urls['finance'] }}">Finance</a>
-            </li>
-
-            <li>
-              <a href="{{ theme_variables.external_urls['optim'] }}">Optimization</a>
-            </li>
-          </ul>
-          <br>
-          <li>
-            <a href="{{ theme_variables.external_urls['experiments'] }}">Experiments</a>
-          </li>
-          <br>
-          <li class="resources-mobile-menu-title">
-            Resources
-          </li>
-
-          <ul class="resources-mobile-menu-items">
-            <li>
-              <a href="{{ theme_variables.external_urls['slack'] }}">Slack support</a>
-            </li>
-          </ul>
-          <br>
-        </ul>
-      </div>
-    </div>
-  </div>
-
-  <!-- End Mobile Menu -->
-
-  <script type="text/javascript" src="{{ pathto('_static/js/vendor/anchor.min.js', 1) }}"></script>
-
-  <script type="text/javascript">
-    $(document).ready(function() {
-      mobileMenu.bind();
-      mobileTOC.bind();
-      pytorchAnchors.bind();
-      sideMenus.bind();
-      scrollToAnchor.bind();
-      highlightNavigation.bind();
-      mainMenuDropdown.bind();
-      filterTags.bind();
-
-      // Add class to links that have code blocks, since we cannot create links in code blocks
-      $("article.pytorch-article a span.pre").each(function(e) {
-        $(this).closest("a").addClass("has-code");
-      });
-    })
-  </script>
-</body>
-</html>
diff --git a/docs/conf.py b/docs/conf.py
index fea1ef788..52707080d 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -1,6 +1,6 @@
 # This code is part of Qiskit.
 #
-# (C) Copyright IBM 2021, 2022.
+# (C) Copyright IBM 2021, 2023.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -86,13 +86,6 @@
 
 # -- General configuration ---------------------------------------------------
 
-# If your documentation needs a minimal Sphinx version, state it here.
-#
-# needs_sphinx = '1.0'
-
-# Add any Sphinx extension module names here, as strings. They can be
-# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
-# ones.
 extensions = [
     "sphinx.ext.napoleon",
     "sphinx.ext.autodoc",
@@ -102,7 +95,6 @@
     "sphinx.ext.extlinks",
     "sphinx_design",
     "jupyter_sphinx",
-    "sphinx_autodoc_typehints",
     "reno.sphinxext",
     "sphinx.ext.doctest",
     "nbsphinx",
@@ -115,13 +107,13 @@
 nbsphinx_timeout = 180
 nbsphinx_execute = os.getenv("QISKIT_DOCS_BUILD_TUTORIALS", "never")
 nbsphinx_widgets_path = ""
-exclude_patterns = ["_build", "**.ipynb_checkpoints"]
 nbsphinx_thumbnails = {
     "tutorials/01_quadratic_program": "_static/1_quadratic_program.png",
     "tutorials/02_converters_for_quadratic_programs": "_static/2_converters.png",
     "tutorials/03_minimum_eigen_optimizer": "_static/3_min_eig_opt.png",
     "tutorials/04_grover_optimizer": "_static/4_grover.png",
     "tutorials/05_admm_optimizer": "_static/5_ADMM.png",
+    "**": "_static/no_image.png",
 }
 
 spelling_word_list_filename = "../.pylintdict"
@@ -137,6 +129,12 @@
 # -----------------------------------------------------------------------------
 # Autodoc
 # -----------------------------------------------------------------------------
+# Move type hints from signatures to the parameter descriptions (except in overload cases, where
+# that's not possible).
+autodoc_typehints = "description"
+# Only add type hints from signature to description body if the parameter has documentation.  The
+# return type is always added to the description (if in the signature).
+autodoc_typehints_description_target = "documented_params"
 
 autodoc_default_options = {
     "inherited-members": None,
@@ -166,7 +164,7 @@
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.
 # This pattern also affects html_static_path and html_extra_path.
-exclude_patterns = ["_build", "**.ipynb_checkpoints"]
+exclude_patterns = ["**site-packages", "_build", "**.ipynb_checkpoints"]
 
 # The name of the Pygments (syntax highlighting) style to use.
 pygments_style = "colorful"
@@ -188,18 +186,7 @@
 
 # -- Options for HTML output -------------------------------------------------
 
-# The theme to use for HTML and HTML Help pages.  See the documentation for
-# a list of builtin themes.
-#
-#
 html_theme = "qiskit_sphinx_theme"
-
-html_theme_path = [".", qiskit_sphinx_theme.get_html_theme_path()]
-
-# Theme options are theme-specific and customize the look and feel of a theme
-# further.  For a list of options available for each theme, see the
-# documentation.
-#
 html_theme_options = {
     "logo_only": False,
     "display_version": True,
@@ -221,6 +208,8 @@
     "docplex.mp": ("https://ibmdecisionoptimization.github.io/docplex-doc/mp", None),
     "qiskit": ("https://qiskit.org/documentation/", None),
 }
+
+html_context = {"analytics_enabled": True}
 # -- Extension configuration -------------------------------------------------
 
 
diff --git a/docs/getting_started.rst b/docs/getting_started.rst
index 070b79b50..c8afe62a0 100644
--- a/docs/getting_started.rst
+++ b/docs/getting_started.rst
@@ -21,8 +21,8 @@ See :ref:`optional_installs` for more information.
 
     .. tab-item:: Start locally
 
-        The simplest way to get started is to follow the getting started 'Start locally' for Qiskit
-        here `Qiskit Getting Started <https://qiskit.org/documentation/getting_started.html>`__
+        The simplest way to get started is to follow the `getting started 'Start locally' guide for 
+        Qiskit <https://qiskit.org/documentation/getting_started.html>`__
 
         In your virtual environment where you installed Qiskit simply add ``optimization`` to the
         extra list in a similar manner to how the extra ``visualization`` support is installed for
diff --git a/docs/index.rst b/docs/index.rst
index 5d600893f..1038540ec 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -27,6 +27,8 @@ Next Steps
 
 `Getting started <getting_started.html>`_
 
+`Migration Guide <migration/index.html>`_
+
 `Tutorials <tutorials/index.html>`_
 
 .. toctree::
@@ -34,6 +36,7 @@ Next Steps
 
     Overview <self>
     Getting Started <getting_started>
+    Migration Guide <migration/index>
     Tutorials <tutorials/index>
     API Reference <apidocs/qiskit_optimization>
     Release Notes <release_notes>
diff --git a/docs/migration/01_migration_guide_to_v0.5.ipynb b/docs/migration/01_migration_guide_to_v0.5.ipynb
new file mode 100644
index 000000000..90a659cf6
--- /dev/null
+++ b/docs/migration/01_migration_guide_to_v0.5.ipynb
@@ -0,0 +1,595 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Qiskit Optimization v0.5 Migration Guide\n",
+    "\n",
+    "This tutorial will guide you through the process of migrating your code from Qiskit Optimization v0.4 to v0.5."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Overview\n",
+    "\n",
+    "Qiskit Terra v0.22 introduces new algorithm implementations that leverage [Qiskit Primitives](https://qiskit.org/documentation/apidoc/primitives.html) (Estimator and Sampler). The former algorithm implementations that leverage opflow will be deprecated in the future release.\n",
+    "\n",
+    "Qiskit Optimization v0.5 supports both the new and the former algorithms of Qiskit Terra v0.22 until the former algorithms are deprecated.\n",
+    "\n",
+    "It is not the intention to provide detailed explanations of the primitives in this migration guide. We suggest that you read the [corresponding resources](https://qiskit.org/documentation/apidoc/primitives.html) of the Qiskit Terra documentation instead.\n",
+    "\n",
+    "We use `qiskit.primitives.Sampler` in this guide as an example of Sampler implementation, which follows `qiskit.primitives.BaseSampler` interface. Users can also use other Sampler implementations such as `BackendSampler` (qiskit-terra), `AerSampler` (qiskit-aer), and Qiskit Runtime Sampler (qiskit-ibm-runtime)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## `MinimumEigenOptimizer`\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The former algorithms exist in `qiskit.algorithms.minimum_eigen_solvers` and we can access them by `qiskit.algorithms.*`. On the other hand, the new algorithms exist in `qiskit.algorithms.minimum_eigensolvers` and we can access them by `qiskit.algorithms.minimum_eigensolvers.*`. Note that the difference is `minimum_eigen_solvers` (former) and `minimum_eigensolvers` (new).\n",
+    "\n",
+    "`MinimumEigenOptimizer` of Qiskit Optimization can use `qiskit.algorithms.MinimumEigenSolver` interface of the former algorithms and \n",
+    "`qiskit.algorithms.minimum_eigensolvers.SamplingMinimumEigensolver` interface of the new algorithms.\n",
+    "Note that `MinimumEigenOptimizer` cannot basically handle `qiskit.algorithms.minimum_eigensolvers.MinimumEigensolver` of the new algorithms.\n",
+    "But there is an exception. `MinimumEigenOptimizer` can handle `algorithms.minimum_eigensolver.NumPyMinimumEigensolver` though it inherits `qiskit.algorithms.minimum_eigensolvers.MinimumEigensolver`. It is because `algorithms.minimum_eigensolver.NumPyMinimumEigensolver` has an extension that allows users to access the eigen states.\n",
+    "\n",
+    "The following is the corresponding table.\n",
+    "\n",
+    "|Former algorithm | New algorithm | \n",
+    "|-----------------|:--------------|\n",
+    "|`qiskit.algorithms.MinimumEigenSolver`|`qiskit.algorithms.minimum_eigensolvers.SamplingMinimumEigensolver`|\n",
+    "|`qiskit.algorithms.NumPyMinimumEigensolver`|`qiskit.algorithms.minimum_eigensolver.NumPyMinimumEigensolver`|\n",
+    "|`qiskit.algorithms.QAOA`|`qiskit.algorithms.minimum_eigensolvers.QAOA`|\n",
+    "|`qiskit.algorithms.VQE`|`qiskit.algorithms.minimum_eigensolvers.SamplingVQE`|"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Setup of a problem"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Problem name: sample\n",
+      "\n",
+      "Maximize\n",
+      "  x - 2*y\n",
+      "\n",
+      "Subject to\n",
+      "  No constraints\n",
+      "\n",
+      "  Binary variables (2)\n",
+      "    x y\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from qiskit_optimization import QuadraticProgram\n",
+    "\n",
+    "problem = QuadraticProgram(\"sample\")\n",
+    "problem.binary_var(\"x\")\n",
+    "problem.binary_var(\"y\")\n",
+    "problem.maximize(linear={\"x\": 1, \"y\": -2})\n",
+    "print(problem.prettyprint())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### NumPyMinimumEigensolver\n",
+    "\n",
+    "Previously"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "fval=1.0, x=1.0, y=0.0, status=SUCCESS\n"
+     ]
+    }
+   ],
+   "source": [
+    "from qiskit.algorithms import NumPyMinimumEigensolver\n",
+    "\n",
+    "from qiskit_optimization.algorithms import MinimumEigenOptimizer\n",
+    "\n",
+    "mes = NumPyMinimumEigensolver()\n",
+    "meo = MinimumEigenOptimizer(min_eigen_solver=mes)\n",
+    "result = meo.solve(problem)\n",
+    "print(result)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "New"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "fval=1.0, x=1.0, y=0.0, status=SUCCESS\n"
+     ]
+    }
+   ],
+   "source": [
+    "from qiskit.algorithms.minimum_eigensolvers import NumPyMinimumEigensolver\n",
+    "\n",
+    "from qiskit_optimization.algorithms import MinimumEigenOptimizer\n",
+    "\n",
+    "mes = NumPyMinimumEigensolver()\n",
+    "meo = MinimumEigenOptimizer(min_eigen_solver=mes)\n",
+    "result = meo.solve(problem)\n",
+    "print(result)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### QAOA\n",
+    "\n",
+    "Previously"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "fval=1.0, x=1.0, y=0.0, status=SUCCESS\n"
+     ]
+    }
+   ],
+   "source": [
+    "from qiskit import BasicAer\n",
+    "from qiskit.algorithms import QAOA\n",
+    "from qiskit.algorithms.optimizers import COBYLA\n",
+    "from qiskit.utils import QuantumInstance\n",
+    "\n",
+    "from qiskit_optimization.algorithms import MinimumEigenOptimizer\n",
+    "\n",
+    "backend = BasicAer.get_backend(\"qasm_simulator\")\n",
+    "shots = 1000\n",
+    "qins = QuantumInstance(backend=backend, shots=shots)\n",
+    "mes = QAOA(optimizer=COBYLA(), quantum_instance=qins)\n",
+    "meo = MinimumEigenOptimizer(min_eigen_solver=mes)\n",
+    "result = meo.solve(problem)\n",
+    "print(result)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "New"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "fval=1.0, x=1.0, y=0.0, status=SUCCESS\n"
+     ]
+    }
+   ],
+   "source": [
+    "from qiskit.algorithms.minimum_eigensolvers import QAOA\n",
+    "from qiskit.algorithms.optimizers import COBYLA\n",
+    "from qiskit.primitives import Sampler\n",
+    "\n",
+    "from qiskit_optimization.algorithms import MinimumEigenOptimizer\n",
+    "\n",
+    "shots = 1000\n",
+    "mes = QAOA(sampler=Sampler(), optimizer=COBYLA())\n",
+    "meo = MinimumEigenOptimizer(min_eigen_solver=mes)\n",
+    "result = meo.solve(problem)\n",
+    "print(result)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### VQE (former) → SamplingVQE (new)\n",
+    "\n",
+    "Previously"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "fval=1.0, x=1.0, y=0.0, status=SUCCESS\n"
+     ]
+    }
+   ],
+   "source": [
+    "from qiskit import BasicAer\n",
+    "from qiskit.algorithms import VQE\n",
+    "from qiskit.algorithms.optimizers import COBYLA\n",
+    "from qiskit.circuit.library import RealAmplitudes\n",
+    "from qiskit.utils import QuantumInstance\n",
+    "\n",
+    "from qiskit_optimization.algorithms import MinimumEigenOptimizer\n",
+    "\n",
+    "backend = BasicAer.get_backend(\"qasm_simulator\")\n",
+    "shots = 1000\n",
+    "qins = QuantumInstance(backend=backend, shots=shots)\n",
+    "mes = VQE(ansatz=RealAmplitudes(), optimizer=COBYLA(), quantum_instance=qins)\n",
+    "meo = MinimumEigenOptimizer(min_eigen_solver=mes)\n",
+    "result = meo.solve(problem)\n",
+    "print(result)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "New"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "fval=1.0, x=1.0, y=0.0, status=SUCCESS\n"
+     ]
+    }
+   ],
+   "source": [
+    "from qiskit.algorithms.minimum_eigensolvers import SamplingVQE\n",
+    "from qiskit.algorithms.optimizers import COBYLA\n",
+    "from qiskit.circuit.library import RealAmplitudes\n",
+    "from qiskit.primitives import Sampler\n",
+    "\n",
+    "from qiskit_optimization.algorithms import MinimumEigenOptimizer\n",
+    "\n",
+    "mes = SamplingVQE(sampler=Sampler(), ansatz=RealAmplitudes(), optimizer=COBYLA())\n",
+    "meo = MinimumEigenOptimizer(min_eigen_solver=mes)\n",
+    "result = meo.solve(problem)\n",
+    "print(result)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "An error occurs due to `VQE` with `Estimator`. You can use `SamplingVQE` with `Sampler` instead (see the previous cell)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MinimumEigenOptimizer does not support this VQE. You can use  qiskit.algorithms.minimum_eigensolvers.SamplingVQE instead.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from qiskit.algorithms.minimum_eigensolvers import VQE\n",
+    "from qiskit.algorithms.optimizers import COBYLA\n",
+    "from qiskit.circuit.library import RealAmplitudes\n",
+    "from qiskit.primitives import Estimator\n",
+    "\n",
+    "from qiskit_optimization.algorithms import MinimumEigenOptimizer\n",
+    "\n",
+    "mes = VQE(estimator=Estimator(), ansatz=RealAmplitudes(), optimizer=COBYLA())\n",
+    "try:\n",
+    "    meo = MinimumEigenOptimizer(min_eigen_solver=mes)\n",
+    "except TypeError as ex:\n",
+    "    print(ex)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## `WarmStartQAOAOptimizer`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`WarmStartQAOAOptimizer` can use both the former `qiskit.algorithms.QAOA` and the new `qiskit.algorithms.minimum_eigensolvers.QAOA` as follows."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Previously"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "fval=1.0, x=1.0, y=0.0, status=SUCCESS\n"
+     ]
+    }
+   ],
+   "source": [
+    "from qiskit import BasicAer\n",
+    "from qiskit.algorithms import QAOA\n",
+    "from qiskit.algorithms.optimizers import COBYLA\n",
+    "from qiskit.utils import QuantumInstance\n",
+    "\n",
+    "from qiskit_optimization.algorithms import WarmStartQAOAOptimizer, SlsqpOptimizer\n",
+    "\n",
+    "backend = BasicAer.get_backend(\"qasm_simulator\")\n",
+    "shots = 1000\n",
+    "qins = QuantumInstance(backend=backend, shots=shots)\n",
+    "qaoa = QAOA(optimizer=COBYLA(), quantum_instance=qins)\n",
+    "optimizer = WarmStartQAOAOptimizer(\n",
+    "    pre_solver=SlsqpOptimizer(), relax_for_pre_solver=True, qaoa=qaoa, epsilon=0.25\n",
+    ")\n",
+    "result = optimizer.solve(problem)\n",
+    "print(result)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "New"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "fval=1.0, x=1.0, y=0.0, status=SUCCESS\n"
+     ]
+    }
+   ],
+   "source": [
+    "from qiskit.algorithms.minimum_eigensolvers import QAOA\n",
+    "from qiskit.algorithms.optimizers import COBYLA\n",
+    "from qiskit.primitives import Sampler\n",
+    "\n",
+    "from qiskit_optimization.algorithms import WarmStartQAOAOptimizer, SlsqpOptimizer\n",
+    "\n",
+    "qaoa = QAOA(sampler=Sampler(), optimizer=COBYLA())\n",
+    "optimizer = WarmStartQAOAOptimizer(\n",
+    "    pre_solver=SlsqpOptimizer(), relax_for_pre_solver=True, qaoa=qaoa, epsilon=0.25\n",
+    ")\n",
+    "result = optimizer.solve(problem)\n",
+    "print(result)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## `GroverOptimizer`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`GroverOptimizer` supports both `QuantumInstance` and `BaseSampler`. But users must specify one of them."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Previously"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "fval=1.0, x=1.0, y=0.0, status=SUCCESS\n"
+     ]
+    }
+   ],
+   "source": [
+    "from qiskit import BasicAer\n",
+    "from qiskit.algorithms.optimizers import COBYLA\n",
+    "from qiskit.utils import QuantumInstance\n",
+    "\n",
+    "from qiskit_optimization.algorithms import GroverOptimizer\n",
+    "\n",
+    "backend = BasicAer.get_backend(\"qasm_simulator\")\n",
+    "shots = 1000\n",
+    "qins = QuantumInstance(backend=backend, shots=shots)\n",
+    "optimizer = GroverOptimizer(num_value_qubits=3, num_iterations=3, quantum_instance=qins)\n",
+    "result = optimizer.solve(problem)\n",
+    "print(result)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "New"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "fval=0.0, x=0.0, y=0.0, status=SUCCESS\n"
+     ]
+    }
+   ],
+   "source": [
+    "from qiskit.algorithms.optimizers import COBYLA\n",
+    "from qiskit.primitives import Sampler\n",
+    "\n",
+    "from qiskit_optimization.algorithms import GroverOptimizer\n",
+    "\n",
+    "optimizer = GroverOptimizer(num_value_qubits=3, num_iterations=3, sampler=Sampler())\n",
+    "result = optimizer.solve(problem)\n",
+    "print(result)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "An error occurs because both `quantum_instance` and `sampler` are set. You can set only one of them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Only one of quantum_instance or sampler can be passed, not both!\n"
+     ]
+    }
+   ],
+   "source": [
+    "from qiskit import BasicAer\n",
+    "from qiskit.algorithms.optimizers import COBYLA\n",
+    "from qiskit.utils import QuantumInstance\n",
+    "from qiskit.primitives import Sampler\n",
+    "\n",
+    "from qiskit_optimization.algorithms import GroverOptimizer\n",
+    "\n",
+    "backend = BasicAer.get_backend(\"qasm_simulator\")\n",
+    "shots = 1000\n",
+    "qins = QuantumInstance(backend=backend, shots=shots)\n",
+    "try:\n",
+    "    optimizer = GroverOptimizer(\n",
+    "        num_value_qubits=3, num_iterations=3, quantum_instance=qins, sampler=Sampler()\n",
+    "    )\n",
+    "    # raises an error because both quantum_instance and sampler are set.\n",
+    "except ValueError as ex:\n",
+    "    print(ex)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td><code>qiskit-terra</code></td><td>0.23.0</td></tr><tr><td><code>qiskit-aer</code></td><td>0.11.1</td></tr><tr><td><code>qiskit-optimization</code></td><td>0.5.0</td></tr><tr><td><code>qiskit-machine-learning</code></td><td>0.6.0</td></tr><tr><th>System information</th></tr><tr><td>Python version</td><td>3.9.15</td></tr><tr><td>Python compiler</td><td>Clang 14.0.0 (clang-1400.0.29.102)</td></tr><tr><td>Python build</td><td>main, Oct 11 2022 22:27:25</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Tue Dec 06 22:08:13 2022 JST</td></tr></table>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of Qiskit</h3><p>&copy; Copyright IBM 2017, 2022.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import qiskit.tools.jupyter\n",
+    "\n",
+    "%qiskit_version_table\n",
+    "%qiskit_copyright"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/migration/index.rst b/docs/migration/index.rst
new file mode 100644
index 000000000..257ca60dd
--- /dev/null
+++ b/docs/migration/index.rst
@@ -0,0 +1,16 @@
+###################################
+Qiskit Optimization Migration Guide
+###################################
+
+
+.. nbgallery::
+    :glob:
+
+    *
+
+
+.. Hiding - Indices and tables
+   :ref:`genindex`
+   :ref:`modindex`
+   :ref:`search`
+
diff --git a/docs/tutorials/03_minimum_eigen_optimizer.ipynb b/docs/tutorials/03_minimum_eigen_optimizer.ipynb
index 9117dd96d..72be4e1dc 100644
--- a/docs/tutorials/03_minimum_eigen_optimizer.ipynb
+++ b/docs/tutorials/03_minimum_eigen_optimizer.ipynb
@@ -21,12 +21,17 @@
     "An interesting class of optimization problems to be addressed by quantum computing are Quadratic Unconstrained Binary Optimization (QUBO) problems.\n",
     "Finding the solution to a QUBO is equivalent to finding the ground state of a corresponding Ising Hamiltonian, which is an important problem not only in optimization, but also in quantum chemistry and physics. For this translation, the binary variables taking values in $\\{0, 1\\}$ are replaced by spin variables taking values in $\\{-1, +1\\}$, which allows one to replace the resulting spin variables by Pauli Z matrices, and thus, an Ising Hamiltonian. For more details on this mapping we refer to [1].\n",
     "\n",
-    "Qiskit provides automatic conversion from a suitable `QuadraticProgram` to an Ising Hamiltonian, which then allows leveraging all the `MinimumEigenSolver` implementations, such as\n",
+    "Qiskit provides automatic conversion from a suitable `QuadraticProgram` to an Ising Hamiltonian, which then allows leveraging all the `SamplingMinimumEigensolver` implementations, such as\n",
     "\n",
-    "- `VQE`,\n",
+    "- `SamplingVQE`,\n",
     "- `QAOA`, or\n",
     "- `NumpyMinimumEigensolver` (classical exact method).\n",
     "\n",
+    "Note 1: `MinimumEigenOptimizer` does not support `qiskit.algorithms.minimum_eigensolver.VQE`. But `qiskit.algorithms.minimum_eigensolver.SamplingVQE`\n",
+    "can be used instead.\n",
+    "\n",
+    "Note 2: `MinimumEigenOptimizer` can use `NumpyMinimumEigensolver` as an exception case though it inherits `MinimumEigensolver` (not `SamplingMinimumEigensolver`).\n",
+    "\n",
     "Qiskit Optimization provides a the `MinimumEigenOptimizer` class, which wraps the translation to an Ising Hamiltonian (in Qiskit Terra also called `Operator`), the call to a `MinimumEigensolver`, and the translation of the results back to an `OptimizationResult`.\n",
     "\n",
     "In the following we first illustrate the conversion from a `QuadraticProgram` to an `Operator` and then show how to use the `MinimumEigenOptimizer` with different `MinimumEigensolver`s to solve a given `QuadraticProgram`.\n",
@@ -55,9 +60,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from qiskit import BasicAer\n",
-    "from qiskit.utils import algorithm_globals, QuantumInstance\n",
-    "from qiskit.algorithms import QAOA, NumPyMinimumEigensolver\n",
+    "from qiskit.utils import algorithm_globals\n",
+    "from qiskit.algorithms.minimum_eigensolvers import QAOA, NumPyMinimumEigensolver\n",
+    "from qiskit.algorithms.optimizers import COBYLA\n",
+    "from qiskit.primitives import Sampler\n",
     "from qiskit_optimization.algorithms import (\n",
     "    MinimumEigenOptimizer,\n",
     "    RecursiveMinimumEigenOptimizer,\n",
@@ -201,12 +207,7 @@
    "outputs": [],
    "source": [
     "algorithm_globals.random_seed = 10598\n",
-    "quantum_instance = QuantumInstance(\n",
-    "    BasicAer.get_backend(\"statevector_simulator\"),\n",
-    "    seed_simulator=algorithm_globals.random_seed,\n",
-    "    seed_transpiler=algorithm_globals.random_seed,\n",
-    ")\n",
-    "qaoa_mes = QAOA(quantum_instance=quantum_instance, initial_point=[0.0, 0.0])\n",
+    "qaoa_mes = QAOA(sampler=Sampler(), optimizer=COBYLA(), initial_point=[0.0, 0.0])\n",
     "exact_mes = NumPyMinimumEigensolver()"
    ]
   },
@@ -301,14 +302,14 @@
      "output_type": "stream",
      "text": [
       "variable order: ['x', 'y', 'z']\n",
-      "SolutionSample(x=array([0., 1., 0.]), fval=-2.0, probability=0.12499999999999994, status=<OptimizationResultStatus.SUCCESS: 0>)\n",
-      "SolutionSample(x=array([0., 0., 0.]), fval=0.0, probability=0.12499999999999994, status=<OptimizationResultStatus.SUCCESS: 0>)\n",
-      "SolutionSample(x=array([1., 1., 0.]), fval=0.0, probability=0.12499999999999994, status=<OptimizationResultStatus.SUCCESS: 0>)\n",
-      "SolutionSample(x=array([1., 0., 0.]), fval=1.0, probability=0.12499999999999994, status=<OptimizationResultStatus.SUCCESS: 0>)\n",
-      "SolutionSample(x=array([0., 0., 1.]), fval=3.0, probability=0.12499999999999994, status=<OptimizationResultStatus.SUCCESS: 0>)\n",
-      "SolutionSample(x=array([1., 0., 1.]), fval=3.0, probability=0.12499999999999994, status=<OptimizationResultStatus.SUCCESS: 0>)\n",
-      "SolutionSample(x=array([0., 1., 1.]), fval=3.0, probability=0.12499999999999994, status=<OptimizationResultStatus.SUCCESS: 0>)\n",
-      "SolutionSample(x=array([1., 1., 1.]), fval=4.0, probability=0.12499999999999994, status=<OptimizationResultStatus.SUCCESS: 0>)\n"
+      "SolutionSample(x=array([0., 1., 0.]), fval=-2.0, probability=0.4410306097905226, status=<OptimizationResultStatus.SUCCESS: 0>)\n",
+      "SolutionSample(x=array([0., 0., 0.]), fval=0.0, probability=0.22763693649873265, status=<OptimizationResultStatus.SUCCESS: 0>)\n",
+      "SolutionSample(x=array([1., 1., 0.]), fval=0.0, probability=0.14136368254300133, status=<OptimizationResultStatus.SUCCESS: 0>)\n",
+      "SolutionSample(x=array([1., 0., 0.]), fval=1.0, probability=0.12574358779906872, status=<OptimizationResultStatus.SUCCESS: 0>)\n",
+      "SolutionSample(x=array([0., 0., 1.]), fval=3.0, probability=0.020510231887331747, status=<OptimizationResultStatus.SUCCESS: 0>)\n",
+      "SolutionSample(x=array([1., 0., 1.]), fval=3.0, probability=0.030444770051099967, status=<OptimizationResultStatus.SUCCESS: 0>)\n",
+      "SolutionSample(x=array([0., 1., 1.]), fval=3.0, probability=0.012349858838771063, status=<OptimizationResultStatus.SUCCESS: 0>)\n",
+      "SolutionSample(x=array([1., 1., 1.]), fval=4.0, probability=0.0009203225914718031, status=<OptimizationResultStatus.SUCCESS: 0>)\n"
      ]
     }
    ],
@@ -353,14 +354,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "SolutionSample(x=array([0., 1., 0.]), fval=-2.0, probability=0.12499999999999994, status=<OptimizationResultStatus.SUCCESS: 0>)\n",
-      "SolutionSample(x=array([0., 0., 0.]), fval=0.0, probability=0.12499999999999994, status=<OptimizationResultStatus.SUCCESS: 0>)\n",
-      "SolutionSample(x=array([1., 1., 0.]), fval=0.0, probability=0.12499999999999994, status=<OptimizationResultStatus.SUCCESS: 0>)\n",
-      "SolutionSample(x=array([1., 0., 0.]), fval=1.0, probability=0.12499999999999994, status=<OptimizationResultStatus.SUCCESS: 0>)\n",
-      "SolutionSample(x=array([0., 0., 1.]), fval=3.0, probability=0.12499999999999994, status=<OptimizationResultStatus.SUCCESS: 0>)\n",
-      "SolutionSample(x=array([1., 0., 1.]), fval=3.0, probability=0.12499999999999994, status=<OptimizationResultStatus.SUCCESS: 0>)\n",
-      "SolutionSample(x=array([0., 1., 1.]), fval=3.0, probability=0.12499999999999994, status=<OptimizationResultStatus.SUCCESS: 0>)\n",
-      "SolutionSample(x=array([1., 1., 1.]), fval=4.0, probability=0.12499999999999994, status=<OptimizationResultStatus.SUCCESS: 0>)\n"
+      "SolutionSample(x=array([0., 1., 0.]), fval=-2.0, probability=0.4410306097905226, status=<OptimizationResultStatus.SUCCESS: 0>)\n",
+      "SolutionSample(x=array([0., 0., 0.]), fval=0.0, probability=0.22763693649873265, status=<OptimizationResultStatus.SUCCESS: 0>)\n",
+      "SolutionSample(x=array([1., 1., 0.]), fval=0.0, probability=0.14136368254300133, status=<OptimizationResultStatus.SUCCESS: 0>)\n",
+      "SolutionSample(x=array([1., 0., 0.]), fval=1.0, probability=0.12574358779906872, status=<OptimizationResultStatus.SUCCESS: 0>)\n",
+      "SolutionSample(x=array([0., 0., 1.]), fval=3.0, probability=0.020510231887331747, status=<OptimizationResultStatus.SUCCESS: 0>)\n",
+      "SolutionSample(x=array([1., 0., 1.]), fval=3.0, probability=0.030444770051099967, status=<OptimizationResultStatus.SUCCESS: 0>)\n",
+      "SolutionSample(x=array([0., 1., 1.]), fval=3.0, probability=0.012349858838771063, status=<OptimizationResultStatus.SUCCESS: 0>)\n"
      ]
     }
    ],
@@ -446,14 +446,13 @@
     {
      "data": {
       "text/plain": [
-       "{'x=0 y=1 z=0': 0.12499999999999994,\n",
-       " 'x=0 y=0 z=0': 0.12499999999999994,\n",
-       " 'x=1 y=1 z=0': 0.12499999999999994,\n",
-       " 'x=1 y=0 z=0': 0.12499999999999994,\n",
-       " 'x=0 y=0 z=1': 0.12499999999999994,\n",
-       " 'x=1 y=0 z=1': 0.12499999999999994,\n",
-       " 'x=0 y=1 z=1': 0.12499999999999994,\n",
-       " 'x=1 y=1 z=1': 0.12499999999999994}"
+       "{'x=0 y=1 z=0': 0.4410306097905226,\n",
+       " 'x=0 y=0 z=0': 0.22763693649873265,\n",
+       " 'x=1 y=1 z=0': 0.14136368254300133,\n",
+       " 'x=1 y=0 z=0': 0.12574358779906872,\n",
+       " 'x=0 y=0 z=1': 0.020510231887331747,\n",
+       " 'x=1 y=0 z=1': 0.030444770051099967,\n",
+       " 'x=0 y=1 z=1': 0.012349858838771063}"
       ]
      },
      "execution_count": 15,
@@ -476,9 +475,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 504x360 with 1 Axes>"
+       "<Figure size 700x500 with 1 Axes>"
       ]
      },
      "execution_count": 16,
@@ -585,9 +584,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 504x360 with 1 Axes>"
+       "<Figure size 700x500 with 1 Axes>"
       ]
      },
      "execution_count": 21,
@@ -607,7 +606,7 @@
     {
      "data": {
       "text/html": [
-       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td><code>qiskit-terra</code></td><td>0.21.0.dev0+dbd3961</td></tr><tr><td><code>qiskit-aer</code></td><td>0.10.4</td></tr><tr><td><code>qiskit-ibmq-provider</code></td><td>0.19.1</td></tr><tr><td><code>qiskit-optimization</code></td><td>0.4.0</td></tr><tr><th>System information</th></tr><tr><td>Python version</td><td>3.10.4</td></tr><tr><td>Python compiler</td><td>GCC 11.2.0</td></tr><tr><td>Python build</td><td>main, Apr  2 2022 09:04:19</td></tr><tr><td>OS</td><td>Linux</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>14.577545166015625</td></tr><tr><td colspan='2'>Wed May 18 16:03:33 2022 JST</td></tr></table>"
+       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td><code>qiskit-terra</code></td><td>0.23.0</td></tr><tr><td><code>qiskit-aer</code></td><td>0.11.1</td></tr><tr><td><code>qiskit-optimization</code></td><td>0.5.0</td></tr><tr><td><code>qiskit-machine-learning</code></td><td>0.6.0</td></tr><tr><th>System information</th></tr><tr><td>Python version</td><td>3.9.15</td></tr><tr><td>Python compiler</td><td>Clang 14.0.0 (clang-1400.0.29.102)</td></tr><tr><td>Python build</td><td>main, Oct 11 2022 22:27:25</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Mon Dec 05 22:42:36 2022 JST</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
diff --git a/docs/tutorials/04_grover_optimizer.ipynb b/docs/tutorials/04_grover_optimizer.ipynb
index b7951998f..1c42bd06a 100644
--- a/docs/tutorials/04_grover_optimizer.ipynb
+++ b/docs/tutorials/04_grover_optimizer.ipynb
@@ -78,14 +78,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from qiskit.algorithms import NumPyMinimumEigensolver\n",
+    "from qiskit.algorithms.minimum_eigensolvers import NumPyMinimumEigensolver\n",
+    "from qiskit.primitives import Sampler\n",
     "from qiskit_optimization.algorithms import GroverOptimizer, MinimumEigenOptimizer\n",
-    "from qiskit_optimization.problems import QuadraticProgram\n",
     "from qiskit_optimization.translators import from_docplex_mp\n",
-    "from qiskit import BasicAer\n",
-    "from docplex.mp.model import Model\n",
-    "\n",
-    "backend = BasicAer.get_backend(\"statevector_simulator\")"
+    "from docplex.mp.model import Model"
    ]
   },
   {
@@ -144,7 +141,7 @@
     }
    ],
    "source": [
-    "grover_optimizer = GroverOptimizer(6, num_iterations=10, quantum_instance=backend)\n",
+    "grover_optimizer = GroverOptimizer(6, num_iterations=10, sampler=Sampler())\n",
     "results = grover_optimizer.solve(qp)\n",
     "print(results.prettyprint())"
    ]
@@ -208,7 +205,7 @@
     {
      "data": {
       "text/html": [
-       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td><code>qiskit-terra</code></td><td>0.21.0.dev0+dbd3961</td></tr><tr><td><code>qiskit-aer</code></td><td>0.10.4</td></tr><tr><td><code>qiskit-ibmq-provider</code></td><td>0.19.1</td></tr><tr><td><code>qiskit-optimization</code></td><td>0.4.0</td></tr><tr><th>System information</th></tr><tr><td>Python version</td><td>3.10.4</td></tr><tr><td>Python compiler</td><td>GCC 11.2.0</td></tr><tr><td>Python build</td><td>main, Apr  2 2022 09:04:19</td></tr><tr><td>OS</td><td>Linux</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>14.577545166015625</td></tr><tr><td colspan='2'>Wed May 18 16:03:53 2022 JST</td></tr></table>"
+       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td><code>qiskit-terra</code></td><td>0.23.0</td></tr><tr><td><code>qiskit-aer</code></td><td>0.11.1</td></tr><tr><td><code>qiskit-optimization</code></td><td>0.5.0</td></tr><tr><td><code>qiskit-machine-learning</code></td><td>0.6.0</td></tr><tr><th>System information</th></tr><tr><td>Python version</td><td>3.9.15</td></tr><tr><td>Python compiler</td><td>Clang 14.0.0 (clang-1400.0.29.102)</td></tr><tr><td>Python build</td><td>main, Oct 11 2022 22:27:25</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Tue Dec 06 21:47:01 2022 JST</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
diff --git a/docs/tutorials/05_admm_optimizer.ipynb b/docs/tutorials/05_admm_optimizer.ipynb
index 0902f6b9e..ee4102b15 100644
--- a/docs/tutorials/05_admm_optimizer.ipynb
+++ b/docs/tutorials/05_admm_optimizer.ipynb
@@ -83,17 +83,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import time\n",
-    "from typing import List, Optional, Any\n",
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
     "from docplex.mp.model import Model\n",
     "\n",
-    "from qiskit import BasicAer\n",
-    "from qiskit.algorithms import QAOA, NumPyMinimumEigensolver\n",
+    "from qiskit.algorithms.minimum_eigensolvers import QAOA, NumPyMinimumEigensolver\n",
+    "from qiskit.algorithms.optimizers import COBYLA\n",
+    "from qiskit.primitives import Sampler\n",
     "from qiskit_optimization.algorithms import CobylaOptimizer, MinimumEigenOptimizer\n",
-    "from qiskit_optimization.problems import QuadraticProgram\n",
     "from qiskit_optimization.algorithms.admm_optimizer import ADMMParameters, ADMMOptimizer\n",
     "from qiskit_optimization.translators import from_docplex_mp\n",
     "\n",
@@ -112,7 +109,7 @@
     "\n",
     "To solve the QUBO problems we can choose between \n",
     "\n",
-    "- `MinimumEigenOptimizer` using different `MinimumEigensolver`, such as `VQE`, `QAOA` or `NumpyMinimumEigensolver` (classical)\n",
+    "- `MinimumEigenOptimizer` using different `MinimumEigensolver`, such as `SamplingVQE`, `QAOA` or `NumpyMinimumEigensolver` (classical)\n",
     "- `GroverOptimizer`\n",
     "- `CplexOptimizer` (classical, if CPLEX is installed)\n",
     "\n",
@@ -134,7 +131,7 @@
     "cobyla = CobylaOptimizer()\n",
     "\n",
     "# define QAOA via the minimum eigen optimizer\n",
-    "qaoa = MinimumEigenOptimizer(QAOA(quantum_instance=BasicAer.get_backend(\"statevector_simulator\")))\n",
+    "qaoa = MinimumEigenOptimizer(QAOA(sampler=Sampler(), optimizer=COBYLA()))\n",
     "\n",
     "# exact QUBO solver as classical benchmark\n",
     "exact = MinimumEigenOptimizer(NumPyMinimumEigensolver())  # to solve QUBOs\n",
@@ -208,7 +205,7 @@
    "source": [
     "## Classical Solution\n",
     "\n",
-    "3-ADMM-H needs a QUBO optimizer to solve the QUBO subproblem, and a continuous optimizer to solve the continuous convex constrained subproblem. We first solve the problem classically: we use the `MinimumEigenOptimizer` with the `NumPyMinimumEigenSolver` as a classical and exact QUBO solver and we use the `CobylaOptimizer` as a continuous convex solver. 3-ADMM-H supports any other suitable solver available in Qiskit. For instance, VQE, QAOA, and GroverOptimizer can be invoked as quantum solvers, as demonstrated later.\n",
+    "3-ADMM-H needs a QUBO optimizer to solve the QUBO subproblem, and a continuous optimizer to solve the continuous convex constrained subproblem. We first solve the problem classically: we use the `MinimumEigenOptimizer` with the `NumPyMinimumEigenSolver` as a classical and exact QUBO solver and we use the `CobylaOptimizer` as a continuous convex solver. 3-ADMM-H supports any other suitable solver available in Qiskit. For instance, `SamplingVQE`, `QAOA`, and `GroverOptimizer` can be invoked as quantum solvers, as demonstrated later.\n",
     "If CPLEX is installed, the `CplexOptimizer` can also be used as both, a QUBO and convex solver."
    ]
   },
@@ -313,14 +310,12 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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vDdPOA64EzgFWAFemR51Oi75ZXT7N1cw6XpF7ECuAbRGxPSIOAtcAq/IjRMS96cZ/jfe1uAC4PiIei4jdwPXAygJrHaevt+I9CDPreEUGxELggVz3jtRvyqaVtEbSJkmbdu3addSFNuqb5Vt+m5kV2gZRtIhYFxGDETHY398/Ze/b51t+m5kVGhA7gdNy3YtSv6KnPWZZI7XbIMyssxUZEBuBZZKWSuomu7nfUIvTbgDOl3Ryapw+P/WbFtlzqb0HYWadrbCAiIgR4HKyDfudwOcjYouktZIuApD0Ikk7gNcDV0vakqZ9DPh7spDZCKxN/aZFX28XB0dq7B+uTtcszcyOO60+D+KoRMR1wHUN/d6de72R7PBRs2nXA+uLrG8io3d03TdMb1fHXStoZgb8kjdSF8U37DMzc0A01ddbf2iQG6rNrHM5IJrwHoSZmQOiqXwbhJlZp3JANOGnypmZOSCaOjG1QfiGfWbWyRwQTfR2lemplLwHYWYdzQExAd+Pycw6nQNiAnN9R1cz63AOiAlkz4RwG4SZdS4HxAR8iMnMOp0DYgJ+LrWZdToHxATm+rnUZtbhHBAT6JtV4Yl9w0REu0sxM2sLB8QE+nq7qNaCvQf9TAgz60wOiAn4hn1m1ukcEBMYux+T2yHMrDM5ICYw13sQZtbhHBAT6JuVHhq01wFhZp3JATGB0UNM3oMwsw7lgJhAnx8aZGYdzgExgT4/E8LMOpwDYgKVcok53WUedxuEmXUoB8RhPOOkWezYvbfdZZiZtYUD4jAWz5/D/Y85IMysMxUaEJJWSrpL0jZJVzQZ3iPpc2n4DyUtSf2XSNon6db0889F1jmRxfNnc++jT/l+TGbWkSpFvbGkMnAVcB6wA9goaSgituZGuwzYHRHPkrQa+Afg4jTspxFxVlH1tWLJ/NnsH67xyJMHOKWvt52lmJlNuyL3IFYA2yJie0QcBK4BVjWMswr4RHp9LfDbklRgTUdk8fw5ANz3qA8zmVnnKTIgFgIP5Lp3pH5Nx4mIEeAJYH4atlTSLZJulPTrBdY5ocXzZwNw76NPtWP2ZmZtVdghpmP0IDAQEY9K+lXgK5LOjIg9+ZEkrQHWAAwMDEx5EQtPmkWlJO5zQJhZBypyD2IncFque1Hq13QcSRVgLvBoRByIiEcBImIz8FPgjMYZRMS6iBiMiMH+/v4pX4BKucSik2dxrw8xmVkHKjIgNgLLJC2V1A2sBoYaxhkCLk2vXwd8MyJCUn9q5EbS6cAyYHuBtU5oYP4c7ndAmFkHKuwQU0SMSLoc2ACUgfURsUXSWmBTRAwBHwM+JWkb8BhZiAC8HFgraRioAW+KiMeKqvVwlsyfzS337yYiOI7az83MCldoG0REXAdc19Dv3bnX+4HXN5nui8AXi6ytVYvnz+HJ/SPs3jvMvDnd7S7HzGza+ErqSSyel53J5IZqM+s0DohJLFlQDwi3Q5hZZ3FATGLRybORfC2EmXUeB8QkervKPGPuLJ/JZGYdxwHRgoF5s70HYWYdxwHRgiULZrsNwsw6jgOiBYvnz+HRpw7y5H4/Xc7MOocDogVjp7p6L8LMOocDogW+7beZdSIHRAt8228z60QOiBbM6anQf2IP23c5IMysczggWrRiyTxu/MkuqjU/n9rMOoMDokXnn3kKP//FAW65f3e7SzEzmxYOiBb91nOeRldZbNjyULtLMTObFg6IFvX1dvHSZy5gw5aHifBhJjOb+RwQR+CCM0/l/sf28u8PPdnuUszMCueAOALnLT8FCR9mMrOO4IA4Av0n9jC4+GQ2bHm43aWYmRXOAXGELjjzVO58cI9v/21mM54D4ghdcOapgA8zmdnM54A4QqfNm81zF/bxyZvu9d1dzWxGc0Achb/93TPZuXsfVw5taXcpZmaFcUAchcEl83jLK5bxpZt3MvTjn7W7HDOzQjggjtJbXvEszh44iXd++XZ2Pr6v3eWYmU05B8RRqpRL/OPFZ1OrBZd9fCPbHvlFu0syM5tSDohjMDB/Nv/7jb/Kw3v2c+H/+i6fuuk+34bDzGaMQgNC0kpJd0naJumKJsN7JH0uDf+hpCW5Ye9I/e+SdEGRdR6L3zijnw1vezkrls7nXV+5gzdc/QM+fdN9PPLk/naXZmZ2TFTUf7ySysBPgPOAHcBG4JKI2Job58+A50fEmyStBl4TERdLWg58FlgBPAP4BnBGRFQnmt/g4GBs2rSpkGVpRUTw6ZvuY/337uWenz+FBMuf3seSBXMYmDebRSfP4tS+Xk7p62XBCT2US6IkKJdEd6VEd7lEpewdOjObXpI2R8Rgs2GVAue7AtgWEdtTEdcAq4CtuXFWAX+bXl8LfFiSUv9rIuIAcI+kben9flBgvcdEEn/wkiW88cWL+cnDv+CrdzzI5vt2s/Vne/j6locYrk4exPXAkFJ4SJRKSmGS/ZRLEAHN3q0kRscDKJVACAmUxqlGcGC4xoGRGrVaUCqJSppnmowIqEVQrQURQVe5RFe5RKVcf6/s90g1GK7WsvGoT5u9rtWCCJCy9ppySVRrwfBIjQPVGqRh2TKJrrKy8TRWr+oF1WuvBbX0/vVlzfcfqcXofKsR6XPKKquUsvorpRKVFM6lkoiAkVqNai2bR7k0/jOUss+jWovR9xyrb2xd1GrBwWpt9POoL1dZGreulNZxSRr9rCLGPjdyn1mpYZ1AjM2vvnzpH7z6PErK1melnM2j/pnUIiiXSqPLV/9k87Vln0WkzyNG10F+fZSUfZbdlRJdZVELGK7WGKnG2LTVgLSclZJG37u+3urrIgIOjNQ4OFJt+A7laiKoRVZoEKPfvZJET1eJnkpWS7m+zgT7D1Z56mCVfQer1CJG+1dK9e9a9h3IPqdSNo/a2PegWsuWpz5NpVz/zmTTB8HwSPruR6T3yr7jY59lMFxL4zT87Qdj662S/ra6ytnfR7WWfY7Zd3Hse0j6HPLrIf+3+NyFc/nIHzbdxh+TIgNiIfBArnsHcM5E40TEiKQngPmp/00N0y5snIGkNcAagIGBgSkr/FhI4tmnnsizTz1xtF+1Fjzy5H4eemI/D+/Zz6NPHaQW2UZlJH2JDo5kP7XIvgj1DXR945d1Z9MofTnGNvtQ33jUx4vsL2p0g1pXksb+qEoaraHxQXn5DclwLRhJG4H6lzvI/tC7ymr4IpO+vBq3cR2p1Ub/OLsr2cavFtnGq5r+OEfDJhjdcDbWVM5tcOobyXL6w88H6egfUKor24DVGK6meaZ5l9JGvDT6vvXPfOwzVArmbN4a/awjshCGbHhXOdsTLKXPtR4q9T/isZrH989viOvLlt9QjH23xoKpvm7yIVqfR32DXYugktaPENUIqql/4/vW5TeG9c9jNMTSRmqkmn2OB0dqlEqiKwVSfdr6OsovQ73u+vT1/j2VMj3pu6hcLY3LNVZPVkc1/XNyYCT9s5P7u5ndVWZOT4XerjIljQVqNR9itWC4mn2vlfvnq3EZ6t+bkerYPwgA3eUSXZWxEK4vUz16JUY3/PXPH8aHXP0zOlitMTyS/X1Uytn3EbJ/cur/uNS/z8DotqP+WUTAwLzZFKHIgChcRKwD1kF2iKnN5UyoXBJPnzuLp8+d1e5SzMxaVuRB753AabnuRalf03EkVYC5wKMtTmtmZgUqMiA2AsskLZXUDawGhhrGGQIuTa9fB3wzsoNzQ8DqdJbTUmAZ8KMCazUzswaFHWJKbQqXAxuAMrA+IrZIWgtsiogh4GPAp1Ij9GNkIUIa7/NkDdojwJsPdwaTmZlNvcJOc51u7T7N1czsl9HhTnP1ifdmZtaUA8LMzJpyQJiZWVMOCDMza2rGNFJL2gXcdwxvsQD4+RSV88uiE5cZOnO5O3GZoTOX+0iXeXFE9DcbMGMC4lhJ2jRRS/5M1YnLDJ253J24zNCZyz2Vy+xDTGZm1pQDwszMmnJAjFnX7gLaoBOXGTpzuTtxmaEzl3vKltltEGZm1pT3IMzMrCkHhJmZNdXxASFppaS7JG2TdEW76ymKpNMkfUvSVklbJL019Z8n6XpJd6ffJ7e71qkmqSzpFkn/lrqXSvphWuefS7ejn1EknSTpWkn/LulOSS+Z6eta0l+k7/Ydkj4rqXcmrmtJ6yU9IumOXL+m61aZD6Xlv03SC49kXh0dEJLKwFXAK4HlwCWSlre3qsKMAP8lIpYDLwbenJb1CuCGiFgG3JC6Z5q3Anfmuv8B+J8R8SxgN3BZW6oq1j8CX4uI5wAvIFv+GbuuJS0E/hwYjIjnkj1iYDUzc11/HFjZ0G+idftKsufpLCN7PPM/HcmMOjoggBXAtojYHhEHgWuAVW2uqRAR8WBE3JxeP0m2wVhItryfSKN9Anh1WwosiKRFwKuAj6ZuAa8Ark2jzMRlngu8nOx5K0TEwYh4nBm+rsmebzMrPZ1yNvAgM3BdR8R3yJ6fkzfRul0FfDIyNwEnSXp6q/Pq9IBYCDyQ696R+s1okpYAZwM/BE6JiAfToIeAU9pVV0E+CPwVkB7/znzg8YgYSd0zcZ0vBXYB/ycdWvuopDnM4HUdETuB9wP3kwXDE8BmZv66rpto3R7TNq7TA6LjSDoB+CLwtojYkx+WHvc6Y857lnQh8EhEbG53LdOsArwQ+KeIOBt4iobDSTNwXZ9M9t/yUuAZwBwOPQzTEaZy3XZ6QOwETst1L0r9ZiRJXWTh8JmI+FLq/XB9lzP9fqRd9RXgZcBFku4lO3z4CrJj8yelwxAwM9f5DmBHRPwwdV9LFhgzeV2fC9wTEbsiYhj4Etn6n+nrum6idXtM27hOD4iNwLJ0pkM3WaPWUJtrKkQ69v4x4M6I+EBu0BBwaXp9KfCv011bUSLiHRGxKCKWkK3bb0bE7wPfAl6XRptRywwQEQ8BD0h6dur122TPd5+x65rs0NKLJc1O3/X6Ms/odZ0z0bodAv4wnc30YuCJ3KGoSXX8ldSSfofsOHUZWB8R/729FRVD0q8B3wVuZ+x4/F+TtUN8Hhggu136GyKisQHsl56k3wT+MiIulHQ62R7FPOAW4I0RcaCN5U05SWeRNcx3A9uBPyb7h3DGrmtJfwdcTHbG3i3AfyQ73j6j1rWkzwK/SXZb74eBK4Gv0GTdprD8MNnhtr3AH0fEppbn1ekBYWZmzXX6ISYzM5uAA8LMzJpyQJiZWVMOCDMza8oBYWZmTTkgzBJJv0i/l0j6vSl+779u6P7+VL6/WREcEGaHWgIcUUDkrtadyLiAiIiXHmFNZtPOAWF2qPcAvy7p1vSMgbKk90namO6p/58hu/hO0nclDZFdtYukr0janJ5LsCb1ew/ZXUZvlfSZ1K++t6L03ndIul3Sxbn3/nbumQ6fSRc9Iek9yp7rcZuk90/7p2MdY7L/esw60RWkq64B0ob+iYh4kaQe4HuSvp7GfSHw3Ii4J3X/SbqCdRawUdIXI+IKSZdHxFlN5vVa4CyyZzYsSNN8Jw07GzgT+BnwPeBlku4EXgM8JyJC0klTu+hmY7wHYTa588nuZ3Mr2a1J5pM9gAXgR7lwAPhzST8GbiK7SdoyDu/XgM9GRDUiHgZuBF6Ue+8dEVEDbiU79PUEsB/4mKTXkt0+wawQDgizyQl4S0SclX6WRkR9D+Kp0ZGy+z2dC7wkIl5Adu+f3mOYb/6eQVWgkp5tsILsDq0XAl87hvc3OywHhNmhngROzHVvAP403S4dSWekB/A0mgvsjoi9kp5D9mjXuuH69A2+C1yc2jn6yZ4E96OJCkvP85gbEdcBf0F2aMqsEG6DMDvUbUA1HSr6ONkzJJYAN6eG4l00f3Tl14A3pXaCu8gOM9WtA26TdHO65Xjdl4GXAD8me8jLX0XEQylgmjkR+FdJvWR7Nm8/qiU0a4Hv5mpmZk35EJOZmTXlgDAzs6YcEGZm1pQDwszMmnJAmJlZUw4IMzNrygFhZmZN/X/RhPB5g8S6IAAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -404,14 +399,12 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -431,7 +424,7 @@
     {
      "data": {
       "text/html": [
-       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td><code>qiskit-terra</code></td><td>0.21.0.dev0+dbd3961</td></tr><tr><td><code>qiskit-aer</code></td><td>0.10.4</td></tr><tr><td><code>qiskit-ibmq-provider</code></td><td>0.19.1</td></tr><tr><td><code>qiskit-optimization</code></td><td>0.4.0</td></tr><tr><th>System information</th></tr><tr><td>Python version</td><td>3.10.4</td></tr><tr><td>Python compiler</td><td>GCC 11.2.0</td></tr><tr><td>Python build</td><td>main, Apr  2 2022 09:04:19</td></tr><tr><td>OS</td><td>Linux</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>14.577545166015625</td></tr><tr><td colspan='2'>Wed May 18 16:04:51 2022 JST</td></tr></table>"
+       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td><code>qiskit-terra</code></td><td>0.23.0</td></tr><tr><td><code>qiskit-aer</code></td><td>0.11.1</td></tr><tr><td><code>qiskit-optimization</code></td><td>0.5.0</td></tr><tr><td><code>qiskit-machine-learning</code></td><td>0.6.0</td></tr><tr><th>System information</th></tr><tr><td>Python version</td><td>3.9.15</td></tr><tr><td>Python compiler</td><td>Clang 14.0.0 (clang-1400.0.29.102)</td></tr><tr><td>Python build</td><td>main, Oct 11 2022 22:27:25</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Tue Dec 06 21:47:54 2022 JST</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
diff --git a/docs/tutorials/06_examples_max_cut_and_tsp.ipynb b/docs/tutorials/06_examples_max_cut_and_tsp.ipynb
index a29aa1384..6b1289b99 100644
--- a/docs/tutorials/06_examples_max_cut_and_tsp.ipynb
+++ b/docs/tutorials/06_examples_max_cut_and_tsp.ipynb
@@ -109,7 +109,6 @@
    "source": [
     "# useful additional packages\n",
     "import matplotlib.pyplot as plt\n",
-    "import matplotlib.axes as axes\n",
     "import numpy as np\n",
     "import networkx as nx\n",
     "\n",
@@ -117,11 +116,11 @@
     "from qiskit.tools.visualization import plot_histogram\n",
     "from qiskit.circuit.library import TwoLocal\n",
     "from qiskit_optimization.applications import Maxcut, Tsp\n",
-    "from qiskit.algorithms import VQE, NumPyMinimumEigensolver\n",
+    "from qiskit.algorithms.minimum_eigensolvers import SamplingVQE, NumPyMinimumEigensolver\n",
     "from qiskit.algorithms.optimizers import SPSA\n",
-    "from qiskit.utils import algorithm_globals, QuantumInstance\n",
-    "from qiskit_optimization.algorithms import MinimumEigenOptimizer\n",
-    "from qiskit_optimization.problems import QuadraticProgram"
+    "from qiskit.utils import algorithm_globals\n",
+    "from qiskit.primitives import Sampler\n",
+    "from qiskit_optimization.algorithms import MinimumEigenOptimizer"
    ]
   },
   {
@@ -138,9 +137,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -238,9 +237,9 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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OHEhgYKDZpYlIHVAYEFO8/fbbzJgxg8mTJ/OXv/zF7HKkmjIzM5k9ezZLlizB29u7KhQEBQWZXZqInAWFAWlwmzZt4v7772fkyJHEx8ebXY7UQnZ2NrNnz2bx4sV4enoycOBABg0apH/3Io2UwoA0qIyMDAYPHqwGRE1ETk4Oc+bMYeHChXh4eDBgwAAGDx5MSEiI2aWJSA0oDEiDUQOipis3N5fU1FQWLFiA1Wqlf//+xMbGEhoaanZpIlINCgPSYF566SWWLVvG9OnTOe+888wuR+rBkSNHqkIBwN13301cXBxhYWEmVyYip6NDh6RBrF27loULF/Loo48qCDRhYWFh/POf/2TlypUMHDiQRYsW0atXL/7zn/+Qm5trdnkicpY0MyC1tmvXLoYMGcL111/Ps88+qwZEbqSgoIC0tDTmz5+P3W7nrrvuYujQoURERJhdmogcQ8sEUq9KSkqIi4vD09OTWbNmqQGOmyooKGDevHnMmzcPm83GXXfdxZAhQ4iMjDS7NBFBYUDqkWEYPPnkk3z22WdqQCQAFBYWMn/+fObOnUt5eTl33HEHQ4cO1aFTIibTngGpNwsWLOD9999n3LhxCgICQFBQEImJiaxcuZIRI0bw7rvv0qdPH1566SUOHz5sdnkicgaaGZAa+fnnn0lMTKR///489NBDZpcjLqq4uJh33nmH1NRUSktL6d27N8OHD6dFixZmlybiVrRMIHXuyJEjDB48mBYtWjB16lQ1IJIzKikpYeHChcyZM4eioiJ69+7NsGHDiI6ONrs0EbegMCB1yul08s9//pPt27eTlpamtWCpkZKSEhYtWsScOXMoKCigZ8+exMfH06pVK7NLE2nSFAakTv3vf/9j2rRpTJ48mcsuu8zscqSRKi0tZfHixcyePZu8vLyqUNC6dWuzSxNpkhQGpM5s2rSJBx54gHvvvZcRI0aYXY40AWVlZSxZsoRZs2Zx5MgRbrvtNuLj47UhVaSOKQxInTh06BCDBg3iggsu4N///rcaEEmdKi8vZ+nSpcyaNYucnBxuueUWRowYQbt27cwuTaRJUBiQs2az2UhMTCQ3N5e0tDR976Xe2Gw2li1bxsyZM8nOzuamm25ixIgRtG/f3uzSRBo1hQE5ay+//DJLly5VAyJpMDabjRUrVpCSkkJmZiY33ngjCQkJdOjQwezSRBolHTokZ2Xt2rUsWLCAhx9+WEFAGoy3tzf9+vVj6dKljBkzhp9++omYmBieeOIJduzYYXZ5Ik2WZgbkBLt27WLo0KFce+21PPfcc2pAJKapqKhg9erVzJgxg/T0dK6//noSExPp3Lmz2aWJNApaJpBaKSkpYejQoVgsFmbNmoWfn5/ZJYlgt9tZs2YN06dP5+DBg1x33XUkJibSpUsXs0sTcWlaJpAaMwyDF154gcOHD/PKK68oCIjL8PT0pHfv3ixevJhx48bx22+/MWjQIB566CG2bt1aredwOBz88ssvFBQU1HO1Io2PwoBUWbhwIevWrWPs2LG6tEtckqenJ7169WLx4sU899xz7Nmzh7i4OB588EF27tx52sf++uuvvPrqq/z1r3/lnnvuIS8vr2GKFmkEFAYEqGxA9PrrrzNgwABuvPFGs8sROS0PDw9uu+02Fi1axPPPP8++ffvYs2cPDofjlI9p2bIlMTEx3H///SQnJzN16lQqKioasGoR16U9A0JeXh6DBw+mefPm/O9//8PLy8vskkRqxOl0VvtArOeff57p06ezatUq/vSnP9VzZSLm0p4BqRan08nYsWMpLy9n4sSJCgLSKJ0pCBz7mefVV19lyJAh2nwocgz1oHVz06ZN44svvmDSpEnqRChNlsPhwNPTk4kTJ+Lr60tMTAze3t5mlyXiMjQz4MY+//xzkpOTueeee7j88svNLkek3nh6Vn7umThxIklJSXTq1OmMj9m2bRvvvPMOu3fvru/yREynMOCmDh06xFNPPUWPHj2Ij483uxyReuN0OoHK47W9vb3p378/Pj4+p7zfvn37GDt2LJdccgkvv/wy3bp1IzExkeLi4gatW6QhKQy4oYqKCp544gn8/Px4/vnn1YlQmiTDMPjggw/4+uuvAXjppZdOe1DR0X8Hzz77LMuXL+fNN9/kk08+YcOGDXzzzTesX7++wWoXaWgaBdyQ3W4nKCiIl156iZCQELPLEakXhmHw4Ycf0qNHD9q3b09FRQUxMTEnzAoYhlF1SeLixYv59NNPSUpKIj4+nsDAQC677DLsdjuffPKJGW9DpEEoDLghb29v3njjDc4//3yzSxGpN1arlQkTJrBt2zauv/56ioqKeOCBB/jmm2+AylAMYLFY8PDwAODNN9/k4osvplevXlXPU1hYSLdu3XRyoTRpCgNuyMPDQ82HxG106dKF6dOns2PHDsLDw5k0aRIAU6ZMYdKkSZSUlACwceNGcnNzuemmm447gTMjI4Mvv/ySbt26AcdfpijSVOjSQhFxCx06dGDx4sWUlZWRn5/PggUL8PLyYtSoUUDloG8YBt27d696jMPhYOXKlWRlZfGPf/wDQEFamiSFATdgGIZ+gIn8ztfXF19fX5YsWUJGRsZxX9++fTvnnntu1dd+/PFH3nzzTUaNGoWvr2+NTjoUaUwUBpqYI0eOsHXrVkpLSwkICOCKK67AYrHoh5jIH0RGRhIZGVn15wsuuIALL7yQJUuWMHjwYDZt2sR//vMfvLy8GD9+PHDmkw5FGiuFgSbE6XRy8803U1xczIEDB4iOjqZ9+/bMmjWLyMhIzRCInEa7du3o3bs3CQkJvPXWW2zbto0rrriCKVOm4OnpedJArZAtTYX+L25CBg4ciJ+fH2vWrOGLL75g7Nix5OTk0LlzZ9577z0FAZHTsFqtjB07lt27dzNw4EDS0tKYP38+f//736tuP8rhcPDrr78yYMAA1q1bV3VgkUhjpTDQRBQWFpKZmUlSUhLt2rWjW7duxMTEsGDBAu6++2769u1LSkqK2WWKuLwWLVowatQobrnlFoKCgk569YCHhwc+Pj60aNGCJ598kpiYGNauXatQII2WwkAT4enpSXZ29nGnpHl4eNCuXTuef/557rnnHmbNmsWhQ4dMrFKk8TnVjNo555zDG2+8wcyZM4mOjuapp57i7rvvZs2aNVWHGIk0FgoDTYSfnx8PP/wwX375JXPnzq06UAUqP+nExcWxadMmvvzySxOrFGl6/vSnP/Hf//6X2bNn07ZtW55++mnuvvtuVq9erVAgjYbCQBNy3XXX0alTJyZPnkxqairZ2dlVt3Xv3p0ePXpQVFRkYoUiTdd5553Hv//9b1JTU2nfvj3jxo2jX79+rFy58rhwLuKKLEY1jtMqKCggJCSE/Px8goODG6IuqaWcnBwSExPZvHkzt912Gz179uSCCy5g+fLl3H///fz000907tzZ7DJFmrzt27eTnJzMRx99RKtWrYiPj+f222+vaqcs0hCqO34rDDQhDoej6oz1V155hdWrV/Prr79SUVFBZGQkjz76qNoVizSw3377jeTkZNavX090dDTDhw+nZ8+eeHl5mV2auAGFATd1bCDYvXs3NpuNvXv30qVLF8455xxzixNxYzt37mTatGl88MEHNG/enPj4eHr27Im3t7fZpUkTpjDQhB05coSwsLCT3qaDhURc265du5g2bRrvv/8+UVFRDBs2jD59+igUSL1QGGiiHnnkEfLy8hgzZgwdO3Y85f02bNhAdHQ0nTp1asDqRKS6du/ezYwZM3jvvfdo1qwZw4YNo2/fvgoFUqcUBpqghQsXMmjQIKxWK5deeikvv/wyl1122Qlrj4cOHWLIkCHk5uaydu1amjVrZlLFInIme/fuZfr06axdu5aIiAiGDh3KHXfcgY+Pj9mlSRNQ3fFblxY2EkVFRSxfvpzHH3+cbdu2UVFRQd++fZk7dy4FBQXH3bdFixYkJibywAMPKAiIuLh27drx3HPPsWjRIi6//HJef/11evfuzdy5cykrKzO7PHETmhloJGw2G4sWLSIoKIhevXoBcN999zFlyhQeeughRo8eTevWrSkvL2fp0qX07dsXX19fk6sWkZo6cOAAM2bMYNWqVYSGhjJkyBDuuusu/Pz8zC5NGiEtEzRBx14pcNTcuXMZPnw41157LRMmTCA1NZVly5axefNm/fAQacTS09OZMWMGK1euJDg4mLi4OPr164e/v7/ZpUkjojDQxB3bOnXXrl306tWLAwcOUFhYyLp166o6rYlI45aens7MmTNZsWIFgYGBxMbG0r9/f4UCqRaFATdxdLZg+/btdO3alTfeeINRo0aZXZaI1LFDhw4xc+ZMli9fjp+fH7GxscTExBAQEGB2aeLCFAbcSF5eHldffTXh4eF8/PHHZpcjIvXo8OHDzJo1i6VLl+Ln58egQYMYMGAAgYGBZpcmLkhXEzRBp8tt/fr1O659sYg0Tc2bN+exxx5j+fLl3HrrrcyYMYNevXoxdepUCgsLzS5PGinNDDQCdrudOXPmEBsbi6en50lPGNTJgyLuKSsrizlz5rBo0SK8vb0ZOHAgAwcO1M9qATQz0KS88cYbvP322+zZs+eUA76CgIh7ioyM5KGHHmLlypX06dOH2bNn06tXL6ZMmXLCGSQip6Iw4OI++OAD5s6dy4MPPqjWwyJyShERETz44IOsXLmSO++8k7S0NHr27MnkyZPJy8szuzxxcVomcGF79+4lLi6Oq666ivHjx+vTv4hUW25uLqmpqSxcuBCA/v37Exsbe8omZ9I06WqCRq60tJShQ4fidDqZPXu2rikWkVrJy8sjLS2Nd955B6fTyd13301cXBzh4eFmlyYNQGGgETMMg3HjxrF+/Xpmz55Nhw4dzC5JRBq5/Px80tLSmD9/Pg6Hg379+jFkyBAiIiLMLk3qkTYQNmJLly5lzZo1PPXUUwoCIlInQkJCGDlyJKtWrSIuLo5ly5bRq1cvXn/9dbKzs80uT0ymmQEXs2XLFkaMGEGfPn144oknzC5HRJqowsJC5s2bx7x58ygvL+fOO+9kyJAhREVFmV2a1CEtEzRCBQUFDB48mLCwMKZNm4a3t7fZJYlIE1dUVMT8+fOrWib37duXYcOGKRQ0EQoDjYzT6eShhx7ip59+Ii0tjZYtW5pdkoi4keLiYt555x1SU1MpLS2lT58+DBs2jBYtWphdmpwF7RloZGbOnMlnn33GCy+8oCAgIg0uICCA+Ph4Vq1axT333MP7779P3759GT9+PBkZGWaXJ/VMMwMu4KuvvmLUqFHEx8dz7733ml2OiAglJSUsWrSIOXPmUFBQQK9evYiPjyc6Otrs0qQGtEzQSGRmZjJ48GA6d+7MpEmTsFo1WSMirqO0tLQqFOTn53P77bczYsQIWrVqZXZpUg0KA42A3W7nnnvuISMjg7S0NJ0MJiIuq6ysjCVLljBr1iyOHDnC7bffTnx8PG3atDG7NDkNhYFG4N///jfz588nOTmZCy+80OxyRETOqLy8vCoU5Obmcsstt5CQkEDbtm3NLk1OQhsIXdz69etJS0tj9OjRCgIi0mj4+PgwcOBAVqxYwcMPP8zXX39Nv379GDt2LHv27DG7PKklzQyYYN++fcTGxnLllVcyYcIENSASkUbLZrOxfPlyUlJSyMrK4qabbmLEiBE6PdVFaJnARZWVlTFs2DBsNhtz5swhICDA7JJERM6azWZj5cqVpKSkcPjwYf7+97+TkJBAx44dzS7NrSkMuCDDMHjmmWf48MMPmTVrlv6RiEiTU1FRwapVq0hJSSE9PZ0bbriBxMREOnXqZHZpbkl7BlzQsmXLWL16NU8++aSCgIg0SV5eXtxxxx0sWbKEsWPHsnXrVgYMGMBjjz3G9u3bzS5PTkEzAw1k27ZtxMfH06tXL8aMGWN2OSIiDcJut7NmzRpmzJjBgQMHuOaaa0hMTKRr165ml+YWtEzgQgoKCoiNjSUkJITp06erAZGIuB2Hw8HatWuZPn06+/bt4+qrryYxMZFu3bqZXVqTpjDgIpxOJw8//DA//vgjqampOspTRNyaw+HgvffeY9q0aezbt4+//vWvJCYmcv7555tdWpOkPQMuYvbs2WzcuJHnnntOQUBE3J6Hhwe33XYbixYt4oUXXuDAgQMMHTqU+++/n59//tns8tyWwkA9+uabb3jrrbeIj4/nr3/9q9nliIi4DKvVyi233MKCBQsYP348hw4dYvjw4YwaNYqffvrJ7PLcjpYJ6klWVhaDBw+mY8eOTJ48WQ2IREROw+l0sn79epKTk9m5cyeXXXYZiYmJXHzxxWaX1qhpz4CJ7HY79957LwcPHiQtLY3w8HCzSxIRaRScTicff/wxycnJ/Pbbb1x66aUkJSXRvXt3s0trlLRnwESTJk3i559/ZuLEiQoCIiI1YLVauf7660lLS+OVV16hoKCApKQkkpKS+Oabb6jG59d6YxgGhXY7WTYb2TYbxQ6HabXUNc0M1LH169fz2GOP8dBDDzFo0CCzyxERadQMw2Djxo1MnTqVbdu2cfHFF5OYmMhf/vKXeu/rYhgG3xcV8UVBAb8UF/N9URHZFRVVgcRqsdDcy4vuQUGcHxDAlcHBnB8Q4FL9ZrRMYIJ9+/YRFxfHFVdcwcSJE13qfwgRkcbMMAw+/fRTkpOT2bJlC3/+859JTEzk8ssvr/OftcUOB6tzckg9fJgfi4qwOZ0AeFut+FgsWC0WDMBpGJQbBhVOJ1gs+Fqt/CUoiEFRUdwSHo6vh0ed1lUbCgMNrKysjOHDh1NeXq4GRCIi9cQwDDZt2sTUqVPZvHkzF1xwAYmJifTo0aNOQsFHR47w9O7d7CgrwwKEeHjgb7We8bkNw6DY6STf4cACnOfvz/gOHbjM5DFTYaABGYbBc889x7p165g1a5YacoiI1DPDMPjiiy9ITk7mp59+4vzzzycpKYkrr7yyVqGgwG5n/N69zM/MpNwwiPL0xKuWV4HZnE6y7Hb8rVbiW7TgwTZt8DdplkAbCBvQ8uXLWblyJU8++aSCgIhIA7BYLPTo0YPp06czefJkPD09eeCBBxg6dCgbNmyo0UbDwzYbg7dsYeahQ3hZLER7edU6CEDlckK0lxcAk9LTGbFtG/l2e62fryFoZuAs/frrrwwfPpyePXvy5JNPml2OiIhbMgyDr7/+muTkZL7//nu6du1KYmIiV1999WlnCrJsNuK2buWHoiKivLzwruMzYcqcTrIrKvhrSAgpXbsS5OlZp89/JlomaACFhYXExsYSFBTEjBkz1IBIRMQFfPvtt0ydOpVvv/2WLl26kJCQwLXXXnvC4W82p5OBW7bwWX5+vQSBo8qdTrIqKrgtIoJp556LtQE3l2uZoJ45nU7GjRtHQUEBL7/8soKAiIiLuOSSS/jf//7H1KlTCQkJ4bHHHmPw4MF8+OGHOH+/MgBgano6nxcU0KwegwCAj9VKuKcn63JzmZ+ZWW+vczYUBmppzpw5bNiwQQ2IRERcVPfu3ZkyZQrTpk0jLCyMxx9/nIEDB/L+++/zc2Ehbx48iPfvlwTWN38PDyzAhH372FdWVu+vV1MKA7Xw7bffMnnyZIYPH87f/vY3s8sREZHTuOiii3jrrbeYMWMGUVFRPDFmDLfPn09WSQkRDbiGH+nlRZbNxiv79zfYa1aX9gzUUHZ2NoMGDaJDhw5MnjwZDxc4VEJERKpv6fffMzQ9HVtREX4WC82aNSPkDGNb6a5d5H/6KSVbt2LLysIjMBC/jh2JuvtuvFu0qPZr59ntWCwWPr7oIlr5+JztWzkj7RmoBw6HgyeffBKr1cr48eMVBEREGqEfQkPxDw2lQ3Q03l5epB88yM6dO8nPzz/lY3JWraLw66/xP+88WsTGEnbddZT8+iu7x46l/MCBar92sIcHRXY7i7Oy6uKt1JmGvcahkZs8eTI//PADU6dOVQMiEZFGqMzhYEl2Nr5WK/4+Pvi3aUNZWRlZ2dmkp6eTnZ1dOVMQEnLc48JvvRW/kSOxHLOsEHz55ex68kmyV66k1T/+Ua3Xt1oseFoszM/M5J+tWrnMsfWaGaimjz/+mNmzZ3P//fdz0UUXmV2OiIjUwvbSUgrsdgKPmdn19fWlTevWtO/QAR8fH9LT09mxcyd5+fkcXUf379z5uCAA4N2iBT6tWmFLT69RDQEeHhyy2ThQXn62b6fOKAxUw/79+xk3bhzXX389gwcPNrscERGppa0lJdicTnxO8onc18eH1r+HAl9fXzLS09m5Ywd5eXknPdHQMAzsBQV4BAXVqAZfq5Uyp5OtJSW1fh91TWHgDMrLy3n88ccJDw/n6aefdpkpHRERqbltJSVgsZz2Z7mvjw+tW7WiQ4cO+Pn5kZGRwc6dO08IBQWbNmE/coTgyy+vUQ2ev7/2NhcKA9ozcAYvvfQSe/fuZebMmQQGBppdjoiInIXsiopq9y3w8fGhVatWNIuMJDsri4yMDLKyswkPCyOgrIxDs2bh16kTIbW8xDy3oqJWj6sPCgOnsWLFClasWMEzzzxD586dzS5HRETOUvHvYcBms+FwOnE6HDgcDpxO53G/V912zO8AJcXFFKSnE7R0KVZ/f1r9859YanFokWEY2GrQTKm+KQycwvbt25k4cSJ9+/alZ8+eZpcjIuL2DMOgtLSUoqIiioqKKCwsrPr96K9jb/vj7UVFRey4/XaOXHABRX+8jNBiwcNqxWq14uHhUfW7t7d35dc9PPCwWsk7fJj8hQtx2my0HzsWr7CwWr8fTxdadlYYOInCwkIeffRR2rdvz2OPPWZ2OSIiTYLT6aS4uPiEgbwmA/qxvQWO5enpSVBQEEFBQQQGBhIYGEhQUBAtWrSo+lpQUBCLw8L4wMuL5mFhxw3yf2xidDJHsrIomD4d78JC2v7rX/i0bl3rvwuLxUKwC51VozDwB4Zh8Mwzz1BQUMBbb72lBkQiIr+z2+0nHahPNpif7Pfi4uJTrtf7+PhUDeZHB++wsDDatm173GD+x8H+6C9vb+9qbfAuO3SIDbt24eflVaMN4aWlpex74w2sGRm0e+wx/M9i6dhpGBiGwbn+/rV+jrqmMPAHc+bM4ZNPPuH111+nVatWZpcjIlJnbDbbKafPTze1fvTPpaWlp3xuf3//EwbzFi1a0KlTpxMG8z8O6IGBgXh5eTXI30FXf388LRZshnHSywtPxul0smvqVIzt2wm//HIcRUXkf/bZcfcJueqqatdQ5nTi6+FBN4UB1/Tdd98xadIkhg4dytVXX212OSIiVY5dL6/NWnlRURE2m+2kz221Wk/6abtdu3an/CR+7EAeGBhYrWl2V9DV3x9/q5USpxOfatZ8MD0d+8GDeHt7U/TDDxT98MMJ96lJGChxOgn19KS9n1+1H1PfFAZ+l52dzZgxY7j44osZOXKk2eWIuAzDMHS+Rh04ul5e06n1Y28/3Xp5cHDwCYP20fXyMw3ofn5+bvM9Dvb05KbwcBZmZhLq4XHG952Tk0NRYSEdn366Ti4vNwyDMqeTO5o1w8OF/s4VBvj/BkQWi0UNiKTJsdvt5OXlkZOTQ1BQEC1btjztD8Di4mLGjRtHSkoKAHFxcTzyyCO0PovNUk3B0fXy6q6V//G2kpKSU66X+/r6nvBpOzw8vGq9/FQD+tH/ru56uVQaEBXF8uxsypxO/E7z876kpITMrCwimjWrs3NmipxO/D08uDsqqk6er6406jBQ7HDwQ1ERW4uL2VJSwraSEkocDgzAz2qli78/5wcE0M3fn4sCAwk+Rd/qt956ix9++IH//e9/RERENOybEKlHOTk5PP/886xdu5bt27czePBgpkyZctIfbEdnAB555BE2btxISkoKoaGhDB8+nOzsbCZPnnxC85bG4uh15bWZWj/657KyslM+f0BAwAnr4seul59sav3Ywb2h1sulUo/gYLr6+/NzcTHRVutJg5TdbufAwYP4+/sTFRlZJ69rGAb5djvXhYZyXkBAnTxnXWmUYeC3khIWZmWxMDOTrIoKHFT+JXtZLFh//6Y6DYMfi4tZmJWFFQj38uLOZs3oHxXFef7+Vd/8DRs2MGvWLB544AEuvvhi896USD1wOp20atWKqVOn8tJLL1FcXHzK+1osFjZv3szGjRtJSEigd+/eQOUpnM8++yxr164lJibGlGWDP66X12Rq/ejXKk5x2tvR9fI/DubNfv80eLJP4scO5I1pvVwqWS0WxrRty/Bt2yhwOAg5yQfFgwcPYoE63Uh+5PcGSY+2bVtnz1lXGlUYyLTZeH7vXtbk5FDscOBjtRLu6YnXac6ZNgwDu2FQYLfzv/R0Zh8+zHWhoTx7zjlYsrN5+umnufbaa4mNjW3gdyNS/yIjI3n00UcBWLhwIT/88AMOh+OE+zmdTqxWKz/++CN+fn78+c9/rrqtR48eBAUF8e2339Y6DDidzqqBuSZT68fedqr1ci8vr5N+6o6Ojj7pJ/E/flJ3p/Vy+X/XhYUxICqKmYcOEeDhcdwBQJmZmZSUltKuXTs862jZ2OZ0Uup08nCbNnSvYWOjhtAowoBhGKzMyeG5PXs4UF5OsIcHraq5RmaxWPCyWIiwWjEMgyKnkzU5OXxdUEDQypVEhoYybtw4/TCQJstut+Pp6UlYWBiFhYXY7fZT3re8vBy73Y7/MZc8hYeHExkZyZ49e076mJycHBYsWHDaAf10MxJH18uP/bQdERHBOeecc8qp9T9eXy5SG0+0bctn+fn8VlpKtLc3VouFwqIicnJyaN68Of51tNvfbhhkVlTQPSiI+1z0knWXDwMOw+DZPXuYeegQDsOgpbd3rXdgWiwWgjw8CLBa2ZmTQ+mllzKwd298XGztRqQuHQ26kZGRFBYWnvLyMoDg4GDKysqOu4+HhwchISEcOnTopI8pLy9n9erVxw3oLVu2pEuXLmc8LCYwMBDPU+zlEalvoV5eTD33XGK3buVgeTkRFgvp6ekEBQcTHh5eJ69hNwwO2Wx09vPj7S5dTrth0Uwu/a/QaRiM2bWL1MOHCbBaCamjTTYFBQXYs7KIjI5mnc3GP3/7jTc7d8Zb637igo6ul59urTwmJuaUu52PhoFmzZpRXFx8QhhwOp1VywRRUVF4eXmRkZFRdXt5eTlOp/OUn8Cjo6NZtWpVHb1bkYbVLSCAmV27Er91K79kZeHl7U10y5Z18twlDge5djvn+vuT0rUrbX196+R564NLh4GJ+/aRevgwQVYrQXX06aGsvJxDGRmEhobSMiSEYoeDFTk5BHp48GrHjloukDp37Hp5dXatn2wd/Uzr5TfddFO1wkBpaekJYcB6zLns7dq1IyoqivXr19O/f3+g8gyOHTt2cOuttx73fCJNxQWBgVz10Ufs8vfH48ILyXE4aHaavWhn4jQMsioqMICbwsOZ2KED0T4+dVt0HXPZMLAhL4/kjAz86jAIOJ1ODh44gI+PDy1atAAgwMMDp2GwMCuLa0JD6d2sWZ28ljQdFRUVZ9VYpaSk5JTP7efnd8JaeLNmzWjfvv0Zry2v6Xp5s2bNqKioOG7PQHl5OevWrcNisdCzZ0/atGnDkCFDiI+P5+qrr+Zvf/sb//3vf9m/fz/x8fGAwoA0PStWrODTBQuY/swzHO7Uif/s389Bmw1fq5VQT89qdxescDo54nBgczqJ9PbmibZtGRgVVXWVmytzyTBQYLfz1O7dlDudtKzD62/T09OxOxy0b9v2uB9oQZ6eFNlsPLdnD5cHB9NcG5KaDMMwKC8vr/FlaMfeVl5efsrnP9nmtlatWp32HPZj/9wQ6+U5OTn8+OOPbNmyhbKyMv773/8SERHBFVdcwd///nfGjx+Pn58fPXv2xGKx0L9/f/Lz83nyySfJyMjg/PPPZ86cObRp06beaxVpaMe2q+/7e7v6v4eFMffwYRZmZZFZUYHTMPC2WvGzWPC2Wjm6oOwEyp1OypxObIaB1WKhpbc3MVFRDIyKoo0LLwv8kcU41ZFYxygoKCAkJIT8/HyCg4PrvagX9+zhzYMHae7tjVcdJaqc3FwyDx+mdZs2BJ1kOtVhGGTYbAxq3pzXO3Wqk9eUs+d0OqvWy2vbKe1Uu+etVutprx8/02ExAQEBjeL68hUrVnDHHXcQGRlZdc10SEgIsbGxxMfHs3z5cvz8/LjpppuqHuN0OikoKMDPzw8fF5/eFKmtwsJC4uLiCAgIICUl5YSZtgK7ndU5OXxRUMB3hYVk2GyU/95xECrPK/CxWGjt60v3wECuCgnhlvBw/F1ok2B1x2+XCwP5djt//f57Cu12mtVgVsBZUUH24sXkf/YZjuJifNq2JapfPwL+9CdKSkvZu3cvEeHhRJ3mCMg8ux2rxcLHF13k8us7jYXD4TjuPPaanv5WnfXyM3VEO9Vgr+vLRdyXYRg8+uijfPvtt6SmplbrcKEsm4195eWU//4zyddq5RxfX8Jd+ATJ6o7fLrdMsCI7m+yKCprX8C83Y+pUCr7+mvCbb8a7eXPyP/2Ufa++SuvHH+eQpyf+fn6nDQIAwR4eZNhsLM7K4p9ufg77UUePcK1tp7QzrZf/ceCOjIykQ4cO1TosRteXi0htzZkzh48//rhG7eojvb2JbKI/d1wuDMw9fBgrVHvDBkDpzp0UfPklUQMGEHHbbQCE/O1v7Bozhr0pKXgnJlbrm221WPC0WJibmcnIVq1cqqNUbRy7Xl7bTmmnuibdYrFUncd+7IDeqlWrak27BwQE6PpyETGF2tWfyKV+GmfZbPxWWkpADddhC7/+GiwWQq+7ruprVi8vrBdfTMXy5bT186v2wBPk4cGh8nJ2lZbS+ZhT2MzgdDopKSk57Vr5mdbRT3b0LBy/Xn7s4B0ZGVmtaXd/f/9GsV4uInKsnJwctas/CZcKA1tLSihzOomo4SfGsj178G7ZEo9jjo4sKiqiJCQET09PLJmZUM1pf1+rlSN2O1tLSs46DDgcjlN+Gq/OgF5UVHTKlqfe3t4nDNghISG0bt36tFPrR7/m6+ur9XIRcSsOh4MxY8aoXf1JuFQY2FZSgoOaLREA2PPz8QwNrfpzRUUFB9PTCWzRglJPT+x5edV+LuvvB01sKynhlj+sl9e0U9rp1sv9/f1P+LQdFRVFx44dq7WjXevlIiI1o3b1p+ZSYSDj9/XpGndEs9mqlgEMw+DAgQN4eHjQPDqa3YaBraSE0tJSHE4nTocDh8Px///9x98dDkoCAvj3Rx+xYOnSk76exWI5brA++nvr1q2rtaM9MDBQiVREpAGpXf3puVQYKHc64cxXOp7A6u2N8fu15KWlpRzJy8Pby4tdBw5gKysjMzeX3GM7rlkseFitWD088LBa8fDwwOrhgZenJ1YPD5ze3px/8cU8eNFFJ93RrvVyEZHG4+DBg2pXfwYuFQasALVYx/YMCaHi96UAbx8f/Pz8Ktu2lpTg6eVFy86dCerYsSoAnOloyIrycjq3bcvtXbrU/E2IiIjLsNlsPPbYY4SqXf1pudTHW38PD2rzbfJp1w5bRgaO0lI8PTzo1rUr7c85B9LTsVdUUOjvj72iAk9Pz2qfER2kaXwRkUbv5ZdfZvfu3bz88ssEBQWZXY7Lcqkw0MnPD+OYox6rK/gvfwHDIO+jj6q+5u/jg9e2bQSeey6EhLBv3z727NlDcXHxaZ/LMAwsFgudjrkyQUREGp+VK1eybNkynnjiCbpopve0XGqZoKu/P95WK+WGgW8NpnL8OnUi6C9/IXPBAhwFBXhFRZH/6afYsrJo98QT+J9zDkXFxWRnZbFv3z78/PxoFhlJYEDACc9lNww8fq9FREQap+3btzNhwgT69OlD7969zS7H5blUb4Iyh4OLv/2Wcoejxmc9OysqyFq0iIJNmyp7E7RpQ+RddxF44YXH3a+4uJisrCxKS0vx9fMjslmz4/rAF9jtOIHPu3dvssdOiog0ZUVFRcTFxeHn50dKSopbN9tqlL0JfD08uCE0lEVZWYT9Pl1fXVYvL5oPHEjzgQNPe7+AgAACAgIqQ0F2Nvv378fX15fIyEgCAwMpdDq5NiREQUBEpBEyDIPnnnuOI0eO8Oabb7p1EKgJlwoDAAOioliRk0OZ04lfPW7iqwoFJSVkZ2Wxf/9+vP398YiIYFDz5vX2uiIiUn/S0tJYv349r732Gq3VcK7aXGoDIcCVISF09ffnyCnO1K9rAf7+tGvXjnbt2mHz9aV0715SR49m/fr1p2ydKyIiruf777/njTfeYMiQIVxzzTVml9OouFwYsFos/LNVKzwtFooaKBAA4ONDaFgYj597LuFBQTz22GMMGjSIDz74QKFARMTFHW1AdNFFF3HfffeZXU6j43JhAKBnRAS9IiLIs9tx1OJEwppyGga5djvXhoby1OWXM2XKFKZNm0ZERARPPPEEAwYMYN26dQoFIiIuyOFw8K9//Qun06kGRLXkkmHAYrEw7pxzaOPjw6GKihqfO1AThmFwuKKCSG9vXmjfvupQoosuuojJkyczY8YMmjdvzpNPPklMTAxr165VKBARcSFvv/023333HRMnTqRZs2Zml9MouWQYAIjy9uaNzp0J8/Qko54CwdEg4O/hwWsdO9L+JAcNXXjhhbz55pukpKTQsmVLnnrqKe6++27WrFmDoyGXMURE5AQbNmwgJSWFUaNG0b17d7PLabRc6pyBk/n4yBHu++03cisqaO7tXeP2xqfiMAwOVVQQ5OHBqx070ruaaXLLli0kJyezceNG2rZty4gRI7jllls0LSUi0sAOHjxIbGws3bt359VXX1XfgZOo7vjt8mEA4KuCAh7csYOdpaUEe3gQ5OFxVt/0IoeDPLud1j4+vNyxI9eHhdX4ObZu3UpycjIbNmygTZs2xMfHc+utt1a1UhYRkfpjs9mIj4+nsLCQ1NRU9R04hSYVBgCOVFQwcd8+3snMpMzpJMTTk0CrtdqhwDAMSpxO8hwOvCwWekVEMO6cc4g6y8OFfv31V5KTk/n4449p1aoV8fHx3H777QoFIiL1aPz48axatYqUlBTOPfdcs8txWU0uDBz10ZEjJGdk8EVBASUOB94WC75WK35WK54WS1U4MAwDu2FQZhiUOZ2UO534Wq10DwoioWVLbg0Pr9Mppe3btzNt2jTWr19PdHQ0w4cPp2fPnnjV8FhlERE5vdWrVzNu3DjGjh1Lnz59zC7HpTXZMHDUluJiFmZm8kFeHhnl5ZQ5nTigqgWyYRhYLRb8rFaivL25NjSU/pGR/DkwsF7XlXbs2MG0adP48MMPad68OcOHD6dXr15463hjEZGztmPHDoYOHcrNN9/M008/bXY5Lq/Jh4FjZdtsbC0pYU9ZGWVOJwbga7XS1seHbgEBRHl5NfjGkl27djFt2jTef/99oqKiGDZsGH369FEoEBGppaKiIoYMGYKvr6/bNyCqLrcKA65s165dzJgxg3Xr1tGsWTOGDRtG3759FQpERGrAMAwef/xxvvzyS1JTU2nTpo3ZJTUK1R2/XfacgaaiQ4cOvPDCCyxcuJBLL72UV199ld69ezNv3jzKy8vNLk9EpFGYN28e69ev55lnnlEQqAeaGWhg+/btY/r06bz77ruEhYUxdOhQ7rzzTnx9fc0uTUTEJf34448kJSUxaNAgHnjgAbPLaVS0TODi9u/fT0pKCqtWrSI0NJQhQ4Zw11134XeSUxBFRNxVbm4ugwcPpnXr1rz99ts64K2GFAYaiYMHDzJjxgxWrVpFcHAwcXFx9OvXD39/f7NLExExldPp5L777mPnzp3MnTtXfQdqQWGgkUlPTyclJYUVK1YQFBREbGws/fv3VygQEbf11ltvMXPmTKZMmcIll1xidjmNksJAI5WRkcHMmTNZvnw5/v7+xMbGEhMTQ0BAgNmliYg0mE8//ZTRo0czatQohg0bZnY5jZbCQCN3+PBhZs6cybJly/Dz82PQoEEMGDCAwMBAs0sTEalX6enpxMbGctFFF/Hqq69iterCt9pSGGgiMjMzq0KBj48PAwcOZODAgWrKISJNks1mY8SIEeTn55Oamqox5ywpDDQxWVlZzJ49m8WLF+Pt7V0VCvT9EJGm5GgDohkzZtC1a1ezy2n0dOhQExMZGcnDDz/MihUr6N27N7Nnz6Znz5689dZb5Ofnm12eiMhZW7NmDUuWLOHRRx9VEGhgmhlopHJzc5k9ezaLFi3CarUSExPD4MGDCQ0NNbs0EZEaO9qA6MYbb2TcuHEN3k+mqdIygZvIzc0lNTWVhQsXAtC/f39iY2MJCwszuTIRkeopLi4mLi4OHx8fUlJSdCJrHVIYcDNHjhwhLS2NBQsW4HQ6ufvuu4mLiyM8PNzs0kRETskwDJ544gm++OIL5syZQ9u2bc0uqUnRngE3ExYWxqhRo1i5ciWDBg1i8eLF9OrVi3//+9/k5OSYXZ6IyEnNmzePDz/8kHHjxikImEgzA01UQUEBc+fOZd68eVRUVNCvXz+GDBmi4zxFxGUcbUA0YMAAHnzwQbPLaZK0TCBA5fdu/vz5zJ07F5vNxp133smQIUOIiooyuzQRcWO5ubnExsYSHR3N22+/jaenp9klNUkKA3KcwsLCqlBQXl5O3759GTZsmEKBiDQ4p9PJqFGj2LFjB2lpaURGRppdUpOlMCAnVVRUxDvvvENaWhqlpaX06dOHYcOG0aJFC7NLExE3MWXKFFJSUnjrrbe49NJLzS6nSVMYkNMqLi5mwYIFpKamUlxcTO/evRk+fDgtW7Y0uzQRacI+++wzHnjgAe677z6GDx9udjlNnsKAVEtJSQkLFy5kzpw5FBYW0qtXL+Lj44mOjja7NBFpYo42IPrzn//Ma6+9pgZEDUBhQGqktLSURYsWMWfOHPLz87n99tsZMWIErVq1Mrs0EWkCbDYbCQkJ5OXlqQFRA1IYkFopKytj8eLFzJo1i7y8PG6//Xbi4+Np06aN2aWJSCM2ceJEli9fzowZM+jWrZvZ5bgNHTokteLr68vgwYNZsWIFo0ePZtOmTdx11108/fTT7Nu3z+zyRKQRevfdd1m0aBGPPvqogoCL0syAnFZ5eTnLli1j5syZ5OTkcPPNNzNixAjOOeccs0sTkUZg165dDBkyhBtuuIFnnnlGDYgamJYJpE7ZbLaqUJCVlcWNN95IQkICHTp0MLs0EXFRJSUlxMXF4enpyaxZs9SAyARaJpA65e3tTf/+/Vm2bBmPP/44P/74IzExMYwZM4adO3eaXZ6IuBjDMDh8+DAOh4NXXnlFQcDFaWZAasVms7Fq1SpSUlLIyMjghhtuICEhgc6dO5tdmoi4CLvdjtPpxNvb2+xS3JaWCaRBVFRUsHr1ambMmEF6ejrXXXcdiYmJdOnSxezSRETcnsKANCi73c6aNWuYPn06Bw8e5JprriExMZGuXbuaXZqIiNtSGBBT2O121q5dy/Tp09m/fz9XX301CQkJnHfeeWaXJiL1yDAMXSngghQGxFQOh4P33nuPadOmsW/fPq666iqSkpI4//zzzS5NRM7SkSNH2Lp1K6WlpQQEBHDFFVcAld0IdcSwa1EYEJfgdDqrQsHevXu58sorSUxM5IILLjC7NBGpBafTyRVXXEFxcTEHDhwgOjqa9u3bM2vWLCIjIzVD4GJ0aaG4BKvVyq233srChQt58cUXycjIYPjw4YwaNYqffvrJ7PJEpIYGDhyIn58fa9as4YsvvmDs2LHk5OTQuXNn3nvvPQWBRkphQBqE1Wrl5ptv5p133mHChAlkZmYSHx/PyJEj+f77780uT0SqobCwkMzMTJKSkmjXrh3dunUjJiaGBQsWcPfdd9O3b19SUlLMLlNqQWFAGpTVauXGG29k/vz5vPTSS+Tm5pKYmMi9997Ld999Z3Z5InIanp6eZGdns379+qqveXh40K5dO55//nnuueceZs2axaFDh0ysUmpDYUBMYbVaueGGG5g7dy4vv/wyBQUFJCUlkZSUxDfffEM1trKISAPz8/Pj4Ycf5ssvv2Tu3LnY7faq21q0aEFcXBybNm3iyy+/NLFKqQ2FATGV1Wrl+uuvJy0tjddee43i4mLuvfdekpKS+OqrrxQKRFzMddddR6dOnZg8eTKpqalkZ2dX3da9e3d69OhBUVGRiRVKbehqAnEphmGwceNGpk2bxpYtW7jwwgtJSkri8ssv18YkEReRk5NDYmIimzdv5rbbbqNnz55ccMEFLF++nPvvv5+ffvpJR5O7CF1aKI2aYRhs2rSJqVOnsnnzZi644AISExPp0aOHQoGIiRwOBx4eHgC88sorrF69ml9//ZWKigoiIyN59NFHiY+PN7lKOUphQJoEwzD4/PPPSU5O5ueff+b8888nMTGRq666SqFAxCTHBoLdu3djs9nYu3cvXbp04ZxzzjG3ODmOwoA0KYZh8OWXXzJ16lR++uknzjvvPBISEvjb3/6mUCBSD44cOUJYWNhJb9PBQo2HDh2SJsVisXDFFVcwffp03nrrLXx8fHjooYeIi4vjk08+qfZGQ4fDwS+//EJBQUE9VyzSeD3yyCM8+uij7Ny586S3Hw0CGzZsYMeOHQ1ZmtQTzQxIo2QYBt9++y1Tp07lu+++o0uXLiQkJHDttdee9mz0LVu28PLLL/Pdd9/Ro0cPXnrpJUJDQxuucBEXt3DhQgYNGoTVauXSSy/l5Zdf5rLLLsPLy+u4+x06dIghQ4aQm5vL2rVradasmUkVy+loZkCaNIvFwqWXXsrUqVOZOnUqwcHBPPbYY3z88cc4nc5TPq5ly5bExMRw//33k5yczNSpU6moqGjAykVcV1FREcuXL+fxxx9n27ZtVFRU0LdvX+bOnXvCbFqLFi1ITEzkgQceUBBoAjzNLkDkbHXv3p23336bzZs3061bt9PODISFhXHrrbfy/PPP07ZtW2677bYTPvGIuCtvb29uu+02goKCaN++PV999RX33Xcfw4cP5+eff2b06NG0bt2a8vJyli5dSt++ffH19TW7bKkDWiYQt3HspqeQkBAeeOABnnrqKby9vU2uTMR1HHulwFFz585l+PDhXHvttUyYMIHU1FSWLVvG5s2b8fPzM6lSqY7qjt+aGRC34XA48PT0ZOLEifj6+hITE6MgIPIHxwYBp9OJ1Wpl0KBBXHHFFfTq1YvrrruOwsJC1q1bpyDQhGjPgLgNT8/K7Dtx4kSSkpLo1KnTGR+zbds23nnnHXbv3l3f5Ym4nKNLbg6Hgw4dOrB06VIKCwt54403+Pvf/25ydVKXFAbELRzdVPjyyy/j7e1N//798fHxOeX99u3bx9ixY7nkkkt4+eWX6datG4mJiRQXFzdo3SKuwMPDg7y8PPr168fVV1/NqFGjzC5J6pjCgDRphmHwwQcf8PXXXwPw0ksvkZiYSJcuXU56/6OfhJ599lmWL1/Om2++ySeffMKGDRv45ptvjmvdKtIUnW4bWb9+/fRvoIlSGJAmzTAMPvzwQ3r06EH79u2pqKggJibmhFkBwzBwOBwALF68mE8//ZSkpCTi4+MJDAzksssuw26388knn5jxNkQaRElJCZs2bTrpbaGhoYwdO/a0V+tI46XvqjRpVquVCRMmsG3bNq6//nqKiop44IEH+OabbwCq+rFbLJaqjVNvvvkmF198Mb169ap6nsLCQrp166aTC6XJMgyDF198kTFjxpCTk3PS++gI4qZLYUDcQpcuXZg+fTo7duwgPDycSZMmATBlyhQmTZpESUkJABs3biQ3N5ebbrqJdu3aVT0+IyODL7/8km7dugGnn0oVaYwWLlzIe++9x1NPPUVERITZ5UgD06WF4lY6dOjA4sWLKSsrIz8/nwULFuDl5VW1ISojIwPDMOjevXvVYxwOBytXriQrK4t//OMfgD4hSdPyyy+/8PrrrxMTE8NNN91kdjliAoUBcUu+vr74+vqyZMkSMjIyjvv69u3bOffcc6u+9uOPP/Lmm28yatQofH19q669FmkK8vPzefzxx+nWrRujR482uxwxiX6iiVuLjIzkwgsvrPrzBRdcwIUXXsiSJUsA2LRpExMnTsTLy4vx48cDKAhIk+F0Ohk7dizl5eVV/5+Le9JPNZFjtGvXjt69e5OQkMBVV11Fr169KC4uZsqUKXh6ep62CZJIYzN9+nQ+//xzXnzxRZo3b252OWIi9SYQOYlDhw6xaNEiOnXqxFVXXUVQUNAJ93E6nRw+fJgPPviAfv366WhWaVS++OIL/vnPf5KUlERiYqLZ5Ug9qe74rTAgUg3HNjk61vLlyxk/fjxBQUHExcVx99134+/vb0KFItV3+PBhBg0axHnnncd///tfLX01YQoDIg0kPT2dmTNnsmLFCgICAoiNjSUmJkahQFxSRUUFSUlJZGZmkpaWRmhoqNklST1SGBBpYIcOHWLmzJksX74cPz8/Bg8eTExMDIGBgWaXJlLl1VdfZdGiRUyfPp3zzz/f7HKknlV3/NbckEgdadGiBU888QTLly/nlltuYdq0afTq1Yvk5GQKCwvNLk+EdevWMX/+fB566CEFATmOZgZE6klmZiazZ89myZIleHt7M2jQIAYMGKB/Q2KKPXv2MGTIEP72t7/xwgsv6OAsN6FlAhEXkZ2dzezZs1m0aBFeXl4MGDCAwYMH69+SNJiSkhKGDRuGYRjMmjVL+1nciMKAiIvJycmpCgUeHh5VoSAkJMTs0qQJMwyDp59+mo8//pjZs2fTvn17s0uSBqQ9AyIuJiIiggcffJCVK1fSr18/5s6dS69evZg0aRJ5eXlmlydN1OLFi3n33XcZO3asgoCckmYGRExy5MgRUlNTWbBgAQB33303sbGxhIeHm1yZNBVbtmxhxIgR3HHHHTz22GNmlyMm0DKBSCORl5dHWloa77zzDk6nk379+jFkyBCFAjkr+fn5DB48mIiICJKTk/H29ja7JDGBwoBII1NQUEBaWhrz5s3D4XBw1113MXToUPWWlxpzOp2MHj2azZs3k5aWRosWLcwuSUyiPQMijUxwcDD/+Mc/WLVqFUOGDGHFihX06tWL1157jaysLLPLk0YkJSWFzz//nBdeeEFBQKpFMwMiLqqwsJB58+Yxb948ysvL6du3L8OGDSMqKsrs0sSFffXVV9x3330kJiaSlJRkdjliMi0TiDQRRUVFzJ8/n7lz51JaWloVCtRyVv4oMzOTQYMG0bVrV9544w01IBKFAZGmpri4mHfeeYfU1FRKSkro06cPw4YNo2XLlmaXJi6goqKCe+65h0OHDjF37lw1IBJAYUCkySopKWHBggXMmTOH4uJievXqxfDhw4mOjja7NDHRa6+9xsKFC0lOTuaCCy4wuxxxEdpAKNJE+fv7M2zYMFauXMnIkSP5+OOPueOOO3j++ec5ePCg2eWJCT744APmzZvHgw8+qCAgtaKZAZFGrrS0lMWLFzN79mzy8vLo2bMnw4cPp02bNmaXJg1g7969xMXF8de//pUXX3xRDYjkOFomEHEzZWVlLFmyhFmzZnHkyBFuvfVWRowYQdu2bc0uTepJaWkpQ4cOxel0Mnv2bDUgkhMoDIi4qfLycpYuXcrMmTPJzc3llltuIT4+nnPOOcfs0qQOGYbBuHHjWL9+PbNnz6ZDhw5mlyQuSHsGRNyUj48PAwYMYMWKFTzyyCN888033H333fzrX/9i9+7dZpcndWTJkiWsWbOGp556SkFAzppmBkSaOJvNxvLly0lJSSErK4sbb7yRhIQEDSCN2NEGRH379uXxxx83uxxxYVomEJHj2Gw2Vq1axYwZMzh8+DA33HADCQkJdOrUyezSpAYKCgoYPHgw4eHhakAkZ6QwICInVVFRwapVq0hJSSE9PZ3rr7+ehIQEunTpYnZpcgZOp5MHH3yQn3/+mbS0NB04JWekPQMiclJeXl7ccccdLFmyhLFjx7Jt2zYGDRrEI488wq+//mp2eXIaM2fOZNOmTbzwwgsKAlKnFAZE3JSnpyd9+vRhyZIljBs3jh07djB48GAeeughtm7danZ58gdfffUVb7/9NiNGjODKK680uxxpYrRMICIAOBwO1q5dy/Tp09m3bx9//etfSUpK4rzzzjO7NLeXmZnJ4MGD6dKlC2+++aYaEEm1ac+AiNSKw+HgvffeY/r06ezdu5crr7ySpKQk/vSnP5ldmluy2+3cc889ZGRkkJaWRlhYmNklSSOiPQMiUiseHh7cdtttLFy4kBdeeIH09HSGDRvGP//5T3766Sezy3M7b7zxBr/88gsTJ05UEJB6ozAgIidltVq55ZZbWLBgAePHj+fw4cPEx8czcuRIfvjhB7PLcwsffvghc+fOZfTo0Vx44YVmlyNNmJYJRKRanE4n69evJzk5mZ07d/KXv/yFxMREunfvbnZpTdK+ffuIjY3lqquuYvz48WpAJLWiPQMiUi+cTicff/wxycnJ/Pbbb1xyySUkJSVxySWXmF1ak1FWVsbQoUOx2+3MmTNHDYik1rRnQETqhdVq5frrryctLY1XX32VoqIi7rnnHpKSkvjqq6+oxucLOQ3DMJgwYQIHDx7klVdeURCQBqEwICK1YrVaufbaa0lNTeX111+ntLSUkSNHkpCQwJdffqlQUEtLly5l9erV/Otf/1L/CGkwCgMiclYsFgtXX301s2fP5j//+Q8VFRXcd999jBgxgs8//1yhoAa2bt3KK6+8wl133cWtt95qdjniRrRnQETqlGEYfP7550ydOpVffvmFP/3pTyQmJnLllVdqE9xpFBQUEBsbS2hoKNOmTVMDIqkT2jMgIqawWCxceeWVpKSkVJ2W98ADDzB06FA2btyomYKTcDqdjBs3jqKiIiZOnKggIA1OYUBE6oXFYqFHjx5Mnz6dt956C29vbx588EGGDBnChg0bFAqOMWvWLDZu3Mjzzz9PdHS02eWIG1IYEJF6ZbFYuOyyy0hOTmbKlCn4+fnx0EMPMXjwYD766COcTqfZJZrqm2++YcqUKcTHx3PVVVeZXY64Ke0ZEJEG9+233zJ16lS+/fZbOnfuTGJiItdee63bNeDJzMwkNjaWTp06MWnSJLd7/1L/dOiQiLi87777juTkZL7++ms6duxIYmIi119/vVsMisc2IEpNTSU8PNzskqQJ0gZCEXF53bt3Z8qUKUybNo1mzZrxxBNPMGDAANatW9fklw8mTZpU1YBIQUDMpjAgIqa76KKLmDx5MjNmzKB58+Y8+eSTxMTEsHbt2iYZCtavX09qaioPPPCAGhCJS9AygYi4nF9++YXk5GQ+++wz2rVrx4gRI7j55pvx8PAwu7Sztm/fPuLi4rjiiiuYOHGizl6QeqU9AyLS6G3ZsoXk5GQ2btxI27ZtGTFiBLfcckujDQVlZWUMGzYMm83GnDlzCAgIMLskaeIUBkSkydi6dSvJycls2LCB1q1bEx8fz2233Yanp2fDFFBRAb/9Blu3Vv7avx9KSsDDAwIDoVMn6Nat8lebNnCST/uGYfDss8/y/vvvM2vWLDp16tQwtYtbUxgQkSbn119/Zdq0aXz00UdER0czYsQIbr/99voLBfv3w+LFMG8eHDoE5eVgtYJh/P+A73RWfg3A1xfOPx8GD4bbb4egoKqnWrZsGS+88ALPPfcct912W/3UK/IHCgMi0mRt376dadOmsX79elq2bMnw4cPp1asXXl5edfMChw/DhAmwahUUFYGXFwQHg4/P/w/8f+RwQGkpFBRU/jkqCpKSICGBbbt2ER8fT8+ePXnyySfrpkaRalAYEJEmb8eOHUybNo0PP/yQ5s2bV4WCWp/tbxiwYgU8/zwcOFC5BBAcfOoAcCp2O+TmQkUFFd27M7qkhPxWrZgxY4b6DkiDUhgQEbexa9cupk2bxvvvv09kZCTDhw+nT58+NRt4bTZ46qnKJQGns/KT/VluVDTKyynctYs8wyDw9dcJv/fes3o+kZpSGBARt7N7926mT5/OunXraNasGUOHDuWOO+44cyiw2eDBB2HJksp1/jr6OZedk0Pm4cN0CAzENyAAnn0Whgypk+cWqQ6FARFxW3v37mX69OmsXbuW8PBwhg4dyp133omPj8+JdzYMeOwxSE2F0FCoo8v9iktK2Ld3LxHNmhHVrBlkZYGnJ7z+OvTtWyevIXImCgMi4vb27dvHjBkzWLNmDWFhYVWhwNfX9//vtHgxjB4N/v7H7f4/GxV2O7t37cLH15e2bdtigcrQcegQhIfDypVwzjl18loip6MwICLyu/3795OSksKqVasIDQ0lLi6Ofv364XfkCPTsCZmZ0KJFnbyWYRjs3bcPm81Gh/btj7/s0emE9HS48UaYNavmGxNFakhhQETkDw4ePMiMGTNYtWoVwcHBvOl00uWXX7BGR5/1ZsGjDmdmkpuTQ7t27fD39z/xDiUlkJ8Pr70GAwbUyWuKnIq6FoqI/EGrVq0YO3YsS5cu5c4LLyTos884WFhIdl4ejjpoiFRQWEhOTg5RzZufPAhA5XKEYcC0aZUzBSIuoIHO8hQRcR3R0dH8IzISR3AwmV5eZGVlkZOTQ0R4OGHh4XicYvq+xOlkTk4Ov5SWsrmsjAKHg3EtW9IrNBSbzUZ6ejrBQUFnbkkcFga//gqffgpXX10P71CkZjQzICLup6QEFizAw8+Pli1b0qljR4KDg8nKzmbHjh1kZWefdKYgz24nOTub3TYbnY+5MsFpGBw4eBBPT09aRkdzxj6Efn6V/Q7eeadu35dILWlmQETczy+/VF7qFxoKgJeXFy1btKBZs2bkZGeTnZ1Nbk4O4eHhhEdEVM0UNPP05L3OnYnw9GRLaSlD9uwB4NChQ9jKyzmnfftTziqcwM+vcmbA4aiz/QoitaWZARFxP9u2VX4y/8NhRF6enrRo0YJOnToREhpKdk4Ov/32G5lZWTgcDrytViL+0BSpqLiYvLw8WrRsie/JzjE4FT+/yj4GvwcKETMpDIiI+9mypbLr4ElaDcPvoaB5czp16kRYaCi5OTn8tmMHmVlZ2B2OqvsZTidHjhwhLDSU0JCQmtXg4wNlZZUtkUVMpmUCEXE/O3dW6xp/L09PmjdvTkREBDm5ueTm5JCbm0t4WBgVvr7YKirw8vOjeW3OKPDwqAwj+/bV4g2I1C2FARFxP6WlNTrwx9PTk+ZRUUSEh1eGgiNH2GOzYRgGEc2aYT3FDMMZWSxQXl67x4rUIYUBEXE/tRy8q0JBRASFR47gk5mJ19ls/jMMnUIoLkH/F4qI+/H3r9zFX0ueHh4EBARgqe2MwLGO7ZMgYhKFARFxP+eeW/mp3Ex2e+Xv7dubW4cICgMi4o66dasMA2YGgrKyylmBrl3Nq0Hkd9ozICLu57zz/v/SPj+/Gj10QW4uhU4nWb9/st9YVETm7/8dExZGYHX3EJSVQbNm0Lp1jV5fpD4oDIiI++nWrXIQ3ru3xmFgTm4uGRUVVX9eX1jI+sJCAG4NDq5eGDCMyjBw443aQCguQWFARNyPtzcMGgTPP1/ZObAGA/LKTp3O/vVLSiqXCPr3P/vnEqkDiqQi4p7uvBOCgyE/v+FfOy8PLrqo8peIC1AYEBH31LIlxMRAcXFln4KGUlhYOTPxj3/U+rwDkbqmMCAi7uuRR6Bz58oOhg1xZYHdXjkTceedcNNN9f96ItWkMCAi7iskBF54ofLKgiNH6ve1DAMOH4ZzzoF//UuzAuJSFAZExL1dcw2MHg02W+Vafn0wDMjIgNBQeP31yksKRVyIwoCIyP33w8iRlZf7ZWfX7ZKBwwHp6ZWzEJMmwZVX1t1zi9QRhQEREYsFxoyBJ58ET084eLBypuBsFRVVzgi0bg3TpsENN5z9c4rUA4UBERGoDAQjR8KCBZWX/GVlQWZmza80MIzKcwQOHqz8/a67YPVquOqqeilbpC7o0CERkWN17w5Ll0JyMsyeXTnFbxgQGFh5UJC394mb/5zOyiWGsrLKAODtDX/+M9x3H/Tsqc2C4vIshnHmxbGCggJCQkLIz88nODi4IeoSETFfSQmsXQvz5sGPP1YO9hUVJw8Dvr4QEFB5xHBMDFx+uY4aFtNVd/zWzICIyKn4+1eeCXDnnZWHBW3bBlu3Vs4WlJdXDvZ+ftChQ2W/g44dK2cFRBoZhQERkeoICoK//KXyl0gTozksERERN6cwICIi4uYUBkRERNycwoCIiIibUxgQERFxcwoDIiIibk5hQERExM0pDIiIiLg5hQERERE3pzAgIiLi5hQGRERE3JzCgIiIiJurVqOio12OCwoK6rUYERERqTtHx+2j4/ipVCsMFBYWAtCmTZuzLEtEREQaWmFhISEhIae83WKcKS4ATqeT9PR0goKCsFgsdVqgiIiI1A/DMCgsLCQ6Ohqr9dQ7A6oVBkRERKTp0gZCERERN6cwICIi4uYUBkRERNycwoCIiIibUxgQERFxcwoDIiIibk5hQERExM39H/nlCZH5SNDXAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -382,15 +381,15 @@
      "text": [
       "energy: -1.5\n",
       "max-cut objective: -4.0\n",
-      "solution: [1 0 1 0]\n",
+      "solution: [1. 0. 1. 0.]\n",
       "solution objective: 4.0\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -427,9 +426,7 @@
    "outputs": [],
    "source": [
     "algorithm_globals.random_seed = 123\n",
-    "seed = 10598\n",
-    "backend = Aer.get_backend(\"aer_simulator_statevector\")\n",
-    "quantum_instance = QuantumInstance(backend, seed_simulator=seed, seed_transpiler=seed)"
+    "seed = 10598"
    ]
   },
   {
@@ -441,18 +438,18 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "energy: -1.4996861455587287\n",
-      "time: 1.5668189525604248\n",
-      "max-cut objective: -3.9996861455587287\n",
-      "solution: [0. 1. 0. 1.]\n",
+      "energy: -1.4996861455587294\n",
+      "time: 5.22296404838562\n",
+      "max-cut objective: -3.999686145558729\n",
+      "solution: [0 1 0 1]\n",
       "solution objective: 4.0\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -460,12 +457,12 @@
     }
    ],
    "source": [
-    "# construct VQE\n",
-    "spsa = SPSA(maxiter=300)\n",
+    "# construct SamplingVQE\n",
+    "optimizer = SPSA(maxiter=300)\n",
     "ry = TwoLocal(qubitOp.num_qubits, \"ry\", \"cz\", reps=5, entanglement=\"linear\")\n",
-    "vqe = VQE(ry, optimizer=spsa, quantum_instance=quantum_instance)\n",
+    "vqe = SamplingVQE(sampler=Sampler(), ansatz=ry, optimizer=optimizer)\n",
     "\n",
-    "# run VQE\n",
+    "# run SamplingVQE\n",
     "result = vqe.compute_minimum_eigenvalue(qubitOp)\n",
     "\n",
     "# print results\n",
@@ -497,9 +494,9 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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OHEhgYKDZpYlIHVAYEFO8/fbbzJgxg8mTJ/OXv/zF7HKkmjIzM5k9ezZLlizB29u7KhQEBQWZXZqInAWFAWlwmzZt4v7772fkyJHEx8ebXY7UQnZ2NrNnz2bx4sV4enoycOBABg0apH/3Io2UwoA0qIyMDAYPHqwGRE1ETk4Oc+bMYeHChXh4eDBgwAAGDx5MSEiI2aWJSA0oDEiDUQOipis3N5fU1FQWLFiA1Wqlf//+xMbGEhoaanZpIlINCgPSYF566SWWLVvG9OnTOe+888wuR+rBkSNHqkIBwN13301cXBxhYWEmVyYip6NDh6RBrF27loULF/Loo48qCDRhYWFh/POf/2TlypUMHDiQRYsW0atXL/7zn/+Qm5trdnkicpY0MyC1tmvXLoYMGcL111/Ps88+qwZEbqSgoIC0tDTmz5+P3W7nrrvuYujQoURERJhdmogcQ8sEUq9KSkqIi4vD09OTWbNmqQGOmyooKGDevHnMmzcPm83GXXfdxZAhQ4iMjDS7NBFBYUDqkWEYPPnkk3z22WdqQCQAFBYWMn/+fObOnUt5eTl33HEHQ4cO1aFTIibTngGpNwsWLOD9999n3LhxCgICQFBQEImJiaxcuZIRI0bw7rvv0qdPH1566SUOHz5sdnkicgaaGZAa+fnnn0lMTKR///489NBDZpcjLqq4uJh33nmH1NRUSktL6d27N8OHD6dFixZmlybiVrRMIHXuyJEjDB48mBYtWjB16lQ1IJIzKikpYeHChcyZM4eioiJ69+7NsGHDiI6ONrs0EbegMCB1yul08s9//pPt27eTlpamtWCpkZKSEhYtWsScOXMoKCigZ8+exMfH06pVK7NLE2nSFAakTv3vf/9j2rRpTJ48mcsuu8zscqSRKi0tZfHixcyePZu8vLyqUNC6dWuzSxNpkhQGpM5s2rSJBx54gHvvvZcRI0aYXY40AWVlZSxZsoRZs2Zx5MgRbrvtNuLj47UhVaSOKQxInTh06BCDBg3iggsu4N///rcaEEmdKi8vZ+nSpcyaNYucnBxuueUWRowYQbt27cwuTaRJUBiQs2az2UhMTCQ3N5e0tDR976Xe2Gw2li1bxsyZM8nOzuamm25ixIgRtG/f3uzSRBo1hQE5ay+//DJLly5VAyJpMDabjRUrVpCSkkJmZiY33ngjCQkJdOjQwezSRBolHTokZ2Xt2rUsWLCAhx9+WEFAGoy3tzf9+vVj6dKljBkzhp9++omYmBieeOIJduzYYXZ5Ik2WZgbkBLt27WLo0KFce+21PPfcc2pAJKapqKhg9erVzJgxg/T0dK6//noSExPp3Lmz2aWJNApaJpBaKSkpYejQoVgsFmbNmoWfn5/ZJYlgt9tZs2YN06dP5+DBg1x33XUkJibSpUsXs0sTcWlaJpAaMwyDF154gcOHD/PKK68oCIjL8PT0pHfv3ixevJhx48bx22+/MWjQIB566CG2bt1aredwOBz88ssvFBQU1HO1Io2PwoBUWbhwIevWrWPs2LG6tEtckqenJ7169WLx4sU899xz7Nmzh7i4OB588EF27tx52sf++uuvvPrqq/z1r3/lnnvuIS8vr2GKFmkEFAYEqGxA9PrrrzNgwABuvPFGs8sROS0PDw9uu+02Fi1axPPPP8++ffvYs2cPDofjlI9p2bIlMTEx3H///SQnJzN16lQqKioasGoR16U9A0JeXh6DBw+mefPm/O9//8PLy8vskkRqxOl0VvtArOeff57p06ezatUq/vSnP9VzZSLm0p4BqRan08nYsWMpLy9n4sSJCgLSKJ0pCBz7mefVV19lyJAh2nwocgz1oHVz06ZN44svvmDSpEnqRChNlsPhwNPTk4kTJ+Lr60tMTAze3t5mlyXiMjQz4MY+//xzkpOTueeee7j88svNLkek3nh6Vn7umThxIklJSXTq1OmMj9m2bRvvvPMOu3fvru/yREynMOCmDh06xFNPPUWPHj2Ij483uxyReuN0OoHK47W9vb3p378/Pj4+p7zfvn37GDt2LJdccgkvv/wy3bp1IzExkeLi4gatW6QhKQy4oYqKCp544gn8/Px4/vnn1YlQmiTDMPjggw/4+uuvAXjppZdOe1DR0X8Hzz77LMuXL+fNN9/kk08+YcOGDXzzzTesX7++wWoXaWgaBdyQ3W4nKCiIl156iZCQELPLEakXhmHw4Ycf0qNHD9q3b09FRQUxMTEnzAoYhlF1SeLixYv59NNPSUpKIj4+nsDAQC677DLsdjuffPKJGW9DpEEoDLghb29v3njjDc4//3yzSxGpN1arlQkTJrBt2zauv/56ioqKeOCBB/jmm2+AylAMYLFY8PDwAODNN9/k4osvplevXlXPU1hYSLdu3XRyoTRpCgNuyMPDQ82HxG106dKF6dOns2PHDsLDw5k0aRIAU6ZMYdKkSZSUlACwceNGcnNzuemmm447gTMjI4Mvv/ySbt26AcdfpijSVOjSQhFxCx06dGDx4sWUlZWRn5/PggUL8PLyYtSoUUDloG8YBt27d696jMPhYOXKlWRlZfGPf/wDQEFamiSFATdgGIZ+gIn8ztfXF19fX5YsWUJGRsZxX9++fTvnnntu1dd+/PFH3nzzTUaNGoWvr2+NTjoUaUwUBpqYI0eOsHXrVkpLSwkICOCKK67AYrHoh5jIH0RGRhIZGVn15wsuuIALL7yQJUuWMHjwYDZt2sR//vMfvLy8GD9+PHDmkw5FGiuFgSbE6XRy8803U1xczIEDB4iOjqZ9+/bMmjWLyMhIzRCInEa7du3o3bs3CQkJvPXWW2zbto0rrriCKVOm4OnpedJArZAtTYX+L25CBg4ciJ+fH2vWrOGLL75g7Nix5OTk0LlzZ9577z0FAZHTsFqtjB07lt27dzNw4EDS0tKYP38+f//736tuP8rhcPDrr78yYMAA1q1bV3VgkUhjpTDQRBQWFpKZmUlSUhLt2rWjW7duxMTEsGDBAu6++2769u1LSkqK2WWKuLwWLVowatQobrnlFoKCgk569YCHhwc+Pj60aNGCJ598kpiYGNauXatQII2WwkAT4enpSXZ29nGnpHl4eNCuXTuef/557rnnHmbNmsWhQ4dMrFKk8TnVjNo555zDG2+8wcyZM4mOjuapp57i7rvvZs2aNVWHGIk0FgoDTYSfnx8PP/wwX375JXPnzq06UAUqP+nExcWxadMmvvzySxOrFGl6/vSnP/Hf//6X2bNn07ZtW55++mnuvvtuVq9erVAgjYbCQBNy3XXX0alTJyZPnkxqairZ2dlVt3Xv3p0ePXpQVFRkYoUiTdd5553Hv//9b1JTU2nfvj3jxo2jX79+rFy58rhwLuKKLEY1jtMqKCggJCSE/Px8goODG6IuqaWcnBwSExPZvHkzt912Gz179uSCCy5g+fLl3H///fz000907tzZ7DJFmrzt27eTnJzMRx99RKtWrYiPj+f222+vaqcs0hCqO34rDDQhDoej6oz1V155hdWrV/Prr79SUVFBZGQkjz76qNoVizSw3377jeTkZNavX090dDTDhw+nZ8+eeHl5mV2auAGFATd1bCDYvXs3NpuNvXv30qVLF8455xxzixNxYzt37mTatGl88MEHNG/enPj4eHr27Im3t7fZpUkTpjDQhB05coSwsLCT3qaDhURc265du5g2bRrvv/8+UVFRDBs2jD59+igUSL1QGGiiHnnkEfLy8hgzZgwdO3Y85f02bNhAdHQ0nTp1asDqRKS6du/ezYwZM3jvvfdo1qwZw4YNo2/fvgoFUqcUBpqghQsXMmjQIKxWK5deeikvv/wyl1122Qlrj4cOHWLIkCHk5uaydu1amjVrZlLFInIme/fuZfr06axdu5aIiAiGDh3KHXfcgY+Pj9mlSRNQ3fFblxY2EkVFRSxfvpzHH3+cbdu2UVFRQd++fZk7dy4FBQXH3bdFixYkJibywAMPKAiIuLh27drx3HPPsWjRIi6//HJef/11evfuzdy5cykrKzO7PHETmhloJGw2G4sWLSIoKIhevXoBcN999zFlyhQeeughRo8eTevWrSkvL2fp0qX07dsXX19fk6sWkZo6cOAAM2bMYNWqVYSGhjJkyBDuuusu/Pz8zC5NGiEtEzRBx14pcNTcuXMZPnw41157LRMmTCA1NZVly5axefNm/fAQacTS09OZMWMGK1euJDg4mLi4OPr164e/v7/ZpUkjojDQxB3bOnXXrl306tWLAwcOUFhYyLp166o6rYlI45aens7MmTNZsWIFgYGBxMbG0r9/f4UCqRaFATdxdLZg+/btdO3alTfeeINRo0aZXZaI1LFDhw4xc+ZMli9fjp+fH7GxscTExBAQEGB2aeLCFAbcSF5eHldffTXh4eF8/PHHZpcjIvXo8OHDzJo1i6VLl+Ln58egQYMYMGAAgYGBZpcmLkhXEzRBp8tt/fr1O659sYg0Tc2bN+exxx5j+fLl3HrrrcyYMYNevXoxdepUCgsLzS5PGinNDDQCdrudOXPmEBsbi6en50lPGNTJgyLuKSsrizlz5rBo0SK8vb0ZOHAgAwcO1M9qATQz0KS88cYbvP322+zZs+eUA76CgIh7ioyM5KGHHmLlypX06dOH2bNn06tXL6ZMmXLCGSQip6Iw4OI++OAD5s6dy4MPPqjWwyJyShERETz44IOsXLmSO++8k7S0NHr27MnkyZPJy8szuzxxcVomcGF79+4lLi6Oq666ivHjx+vTv4hUW25uLqmpqSxcuBCA/v37Exsbe8omZ9I06WqCRq60tJShQ4fidDqZPXu2rikWkVrJy8sjLS2Nd955B6fTyd13301cXBzh4eFmlyYNQGGgETMMg3HjxrF+/Xpmz55Nhw4dzC5JRBq5/Px80tLSmD9/Pg6Hg379+jFkyBAiIiLMLk3qkTYQNmJLly5lzZo1PPXUUwoCIlInQkJCGDlyJKtWrSIuLo5ly5bRq1cvXn/9dbKzs80uT0ymmQEXs2XLFkaMGEGfPn144oknzC5HRJqowsJC5s2bx7x58ygvL+fOO+9kyJAhREVFmV2a1CEtEzRCBQUFDB48mLCwMKZNm4a3t7fZJYlIE1dUVMT8+fOrWib37duXYcOGKRQ0EQoDjYzT6eShhx7ip59+Ii0tjZYtW5pdkoi4keLiYt555x1SU1MpLS2lT58+DBs2jBYtWphdmpwF7RloZGbOnMlnn33GCy+8oCAgIg0uICCA+Ph4Vq1axT333MP7779P3759GT9+PBkZGWaXJ/VMMwMu4KuvvmLUqFHEx8dz7733ml2OiAglJSUsWrSIOXPmUFBQQK9evYiPjyc6Otrs0qQGtEzQSGRmZjJ48GA6d+7MpEmTsFo1WSMirqO0tLQqFOTn53P77bczYsQIWrVqZXZpUg0KA42A3W7nnnvuISMjg7S0NJ0MJiIuq6ysjCVLljBr1iyOHDnC7bffTnx8PG3atDG7NDkNhYFG4N///jfz588nOTmZCy+80OxyRETOqLy8vCoU5Obmcsstt5CQkEDbtm3NLk1OQhsIXdz69etJS0tj9OjRCgIi0mj4+PgwcOBAVqxYwcMPP8zXX39Nv379GDt2LHv27DG7PKklzQyYYN++fcTGxnLllVcyYcIENSASkUbLZrOxfPlyUlJSyMrK4qabbmLEiBE6PdVFaJnARZWVlTFs2DBsNhtz5swhICDA7JJERM6azWZj5cqVpKSkcPjwYf7+97+TkJBAx44dzS7NrSkMuCDDMHjmmWf48MMPmTVrlv6RiEiTU1FRwapVq0hJSSE9PZ0bbriBxMREOnXqZHZpbkl7BlzQsmXLWL16NU8++aSCgIg0SV5eXtxxxx0sWbKEsWPHsnXrVgYMGMBjjz3G9u3bzS5PTkEzAw1k27ZtxMfH06tXL8aMGWN2OSIiDcJut7NmzRpmzJjBgQMHuOaaa0hMTKRr165ml+YWtEzgQgoKCoiNjSUkJITp06erAZGIuB2Hw8HatWuZPn06+/bt4+qrryYxMZFu3bqZXVqTpjDgIpxOJw8//DA//vgjqampOspTRNyaw+HgvffeY9q0aezbt4+//vWvJCYmcv7555tdWpOkPQMuYvbs2WzcuJHnnntOQUBE3J6Hhwe33XYbixYt4oUXXuDAgQMMHTqU+++/n59//tns8tyWwkA9+uabb3jrrbeIj4/nr3/9q9nliIi4DKvVyi233MKCBQsYP348hw4dYvjw4YwaNYqffvrJ7PLcjpYJ6klWVhaDBw+mY8eOTJ48WQ2IREROw+l0sn79epKTk9m5cyeXXXYZiYmJXHzxxWaX1qhpz4CJ7HY79957LwcPHiQtLY3w8HCzSxIRaRScTicff/wxycnJ/Pbbb1x66aUkJSXRvXt3s0trlLRnwESTJk3i559/ZuLEiQoCIiI1YLVauf7660lLS+OVV16hoKCApKQkkpKS+Oabb6jG59d6YxgGhXY7WTYb2TYbxQ6HabXUNc0M1LH169fz2GOP8dBDDzFo0CCzyxERadQMw2Djxo1MnTqVbdu2cfHFF5OYmMhf/vKXeu/rYhgG3xcV8UVBAb8UF/N9URHZFRVVgcRqsdDcy4vuQUGcHxDAlcHBnB8Q4FL9ZrRMYIJ9+/YRFxfHFVdcwcSJE13qfwgRkcbMMAw+/fRTkpOT2bJlC3/+859JTEzk8ssvr/OftcUOB6tzckg9fJgfi4qwOZ0AeFut+FgsWC0WDMBpGJQbBhVOJ1gs+Fqt/CUoiEFRUdwSHo6vh0ed1lUbCgMNrKysjOHDh1NeXq4GRCIi9cQwDDZt2sTUqVPZvHkzF1xwAYmJifTo0aNOQsFHR47w9O7d7CgrwwKEeHjgb7We8bkNw6DY6STf4cACnOfvz/gOHbjM5DFTYaABGYbBc889x7p165g1a5YacoiI1DPDMPjiiy9ITk7mp59+4vzzzycpKYkrr7yyVqGgwG5n/N69zM/MpNwwiPL0xKuWV4HZnE6y7Hb8rVbiW7TgwTZt8DdplkAbCBvQ8uXLWblyJU8++aSCgIhIA7BYLPTo0YPp06czefJkPD09eeCBBxg6dCgbNmyo0UbDwzYbg7dsYeahQ3hZLER7edU6CEDlckK0lxcAk9LTGbFtG/l2e62fryFoZuAs/frrrwwfPpyePXvy5JNPml2OiIhbMgyDr7/+muTkZL7//nu6du1KYmIiV1999WlnCrJsNuK2buWHoiKivLzwruMzYcqcTrIrKvhrSAgpXbsS5OlZp89/JlomaACFhYXExsYSFBTEjBkz1IBIRMQFfPvtt0ydOpVvv/2WLl26kJCQwLXXXnvC4W82p5OBW7bwWX5+vQSBo8qdTrIqKrgtIoJp556LtQE3l2uZoJ45nU7GjRtHQUEBL7/8soKAiIiLuOSSS/jf//7H1KlTCQkJ4bHHHmPw4MF8+OGHOH+/MgBgano6nxcU0KwegwCAj9VKuKcn63JzmZ+ZWW+vczYUBmppzpw5bNiwQQ2IRERcVPfu3ZkyZQrTpk0jLCyMxx9/nIEDB/L+++/zc2Ehbx48iPfvlwTWN38PDyzAhH372FdWVu+vV1MKA7Xw7bffMnnyZIYPH87f/vY3s8sREZHTuOiii3jrrbeYMWMGUVFRPDFmDLfPn09WSQkRDbiGH+nlRZbNxiv79zfYa1aX9gzUUHZ2NoMGDaJDhw5MnjwZDxc4VEJERKpv6fffMzQ9HVtREX4WC82aNSPkDGNb6a5d5H/6KSVbt2LLysIjMBC/jh2JuvtuvFu0qPZr59ntWCwWPr7oIlr5+JztWzkj7RmoBw6HgyeffBKr1cr48eMVBEREGqEfQkPxDw2lQ3Q03l5epB88yM6dO8nPzz/lY3JWraLw66/xP+88WsTGEnbddZT8+iu7x46l/MCBar92sIcHRXY7i7Oy6uKt1JmGvcahkZs8eTI//PADU6dOVQMiEZFGqMzhYEl2Nr5WK/4+Pvi3aUNZWRlZ2dmkp6eTnZ1dOVMQEnLc48JvvRW/kSOxHLOsEHz55ex68kmyV66k1T/+Ua3Xt1oseFoszM/M5J+tWrnMsfWaGaimjz/+mNmzZ3P//fdz0UUXmV2OiIjUwvbSUgrsdgKPmdn19fWlTevWtO/QAR8fH9LT09mxcyd5+fkcXUf379z5uCAA4N2iBT6tWmFLT69RDQEeHhyy2ThQXn62b6fOKAxUw/79+xk3bhzXX389gwcPNrscERGppa0lJdicTnxO8onc18eH1r+HAl9fXzLS09m5Ywd5eXknPdHQMAzsBQV4BAXVqAZfq5Uyp5OtJSW1fh91TWHgDMrLy3n88ccJDw/n6aefdpkpHRERqbltJSVgsZz2Z7mvjw+tW7WiQ4cO+Pn5kZGRwc6dO08IBQWbNmE/coTgyy+vUQ2ev7/2NhcKA9ozcAYvvfQSe/fuZebMmQQGBppdjoiInIXsiopq9y3w8fGhVatWNIuMJDsri4yMDLKyswkPCyOgrIxDs2bh16kTIbW8xDy3oqJWj6sPCgOnsWLFClasWMEzzzxD586dzS5HRETOUvHvYcBms+FwOnE6HDgcDpxO53G/V912zO8AJcXFFKSnE7R0KVZ/f1r9859YanFokWEY2GrQTKm+KQycwvbt25k4cSJ9+/alZ8+eZpcjIuL2DMOgtLSUoqIiioqKKCwsrPr96K9jb/vj7UVFRey4/XaOXHABRX+8jNBiwcNqxWq14uHhUfW7t7d35dc9PPCwWsk7fJj8hQtx2my0HzsWr7CwWr8fTxdadlYYOInCwkIeffRR2rdvz2OPPWZ2OSIiTYLT6aS4uPiEgbwmA/qxvQWO5enpSVBQEEFBQQQGBhIYGEhQUBAtWrSo+lpQUBCLw8L4wMuL5mFhxw3yf2xidDJHsrIomD4d78JC2v7rX/i0bl3rvwuLxUKwC51VozDwB4Zh8Mwzz1BQUMBbb72lBkQiIr+z2+0nHahPNpif7Pfi4uJTrtf7+PhUDeZHB++wsDDatm173GD+x8H+6C9vb+9qbfAuO3SIDbt24eflVaMN4aWlpex74w2sGRm0e+wx/M9i6dhpGBiGwbn+/rV+jrqmMPAHc+bM4ZNPPuH111+nVatWZpcjIlJnbDbbKafPTze1fvTPpaWlp3xuf3//EwbzFi1a0KlTpxMG8z8O6IGBgXh5eTXI30FXf388LRZshnHSywtPxul0smvqVIzt2wm//HIcRUXkf/bZcfcJueqqatdQ5nTi6+FBN4UB1/Tdd98xadIkhg4dytVXX212OSIiVY5dL6/NWnlRURE2m+2kz221Wk/6abtdu3an/CR+7EAeGBhYrWl2V9DV3x9/q5USpxOfatZ8MD0d+8GDeHt7U/TDDxT98MMJ96lJGChxOgn19KS9n1+1H1PfFAZ+l52dzZgxY7j44osZOXKk2eWIuAzDMHS+Rh04ul5e06n1Y28/3Xp5cHDwCYP20fXyMw3ofn5+bvM9Dvb05KbwcBZmZhLq4XHG952Tk0NRYSEdn366Ti4vNwyDMqeTO5o1w8OF/s4VBvj/BkQWi0UNiKTJsdvt5OXlkZOTQ1BQEC1btjztD8Di4mLGjRtHSkoKAHFxcTzyyCO0PovNUk3B0fXy6q6V//G2kpKSU66X+/r6nvBpOzw8vGq9/FQD+tH/ru56uVQaEBXF8uxsypxO/E7z876kpITMrCwimjWrs3NmipxO/D08uDsqqk6er6406jBQ7HDwQ1ERW4uL2VJSwraSEkocDgzAz2qli78/5wcE0M3fn4sCAwk+Rd/qt956ix9++IH//e9/RERENOybEKlHOTk5PP/886xdu5bt27czePBgpkyZctIfbEdnAB555BE2btxISkoKoaGhDB8+nOzsbCZPnnxC85bG4uh15bWZWj/657KyslM+f0BAwAnr4seul59sav3Ywb2h1sulUo/gYLr6+/NzcTHRVutJg5TdbufAwYP4+/sTFRlZJ69rGAb5djvXhYZyXkBAnTxnXWmUYeC3khIWZmWxMDOTrIoKHFT+JXtZLFh//6Y6DYMfi4tZmJWFFQj38uLOZs3oHxXFef7+Vd/8DRs2MGvWLB544AEuvvhi896USD1wOp20atWKqVOn8tJLL1FcXHzK+1osFjZv3szGjRtJSEigd+/eQOUpnM8++yxr164lJibGlGWDP66X12Rq/ejXKk5x2tvR9fI/DubNfv80eLJP4scO5I1pvVwqWS0WxrRty/Bt2yhwOAg5yQfFgwcPYoE63Uh+5PcGSY+2bVtnz1lXGlUYyLTZeH7vXtbk5FDscOBjtRLu6YnXac6ZNgwDu2FQYLfzv/R0Zh8+zHWhoTx7zjlYsrN5+umnufbaa4mNjW3gdyNS/yIjI3n00UcBWLhwIT/88AMOh+OE+zmdTqxWKz/++CN+fn78+c9/rrqtR48eBAUF8e2339Y6DDidzqqBuSZT68fedqr1ci8vr5N+6o6Ojj7pJ/E/flJ3p/Vy+X/XhYUxICqKmYcOEeDhcdwBQJmZmZSUltKuXTs862jZ2OZ0Uup08nCbNnSvYWOjhtAowoBhGKzMyeG5PXs4UF5OsIcHraq5RmaxWPCyWIiwWjEMgyKnkzU5OXxdUEDQypVEhoYybtw4/TCQJstut+Pp6UlYWBiFhYXY7fZT3re8vBy73Y7/MZc8hYeHExkZyZ49e076mJycHBYsWHDaAf10MxJH18uP/bQdERHBOeecc8qp9T9eXy5SG0+0bctn+fn8VlpKtLc3VouFwqIicnJyaN68Of51tNvfbhhkVlTQPSiI+1z0knWXDwMOw+DZPXuYeegQDsOgpbd3rXdgWiwWgjw8CLBa2ZmTQ+mllzKwd298XGztRqQuHQ26kZGRFBYWnvLyMoDg4GDKysqOu4+HhwchISEcOnTopI8pLy9n9erVxw3oLVu2pEuXLmc8LCYwMBDPU+zlEalvoV5eTD33XGK3buVgeTkRFgvp6ekEBQcTHh5eJ69hNwwO2Wx09vPj7S5dTrth0Uwu/a/QaRiM2bWL1MOHCbBaCamjTTYFBQXYs7KIjI5mnc3GP3/7jTc7d8Zb637igo6ul59urTwmJuaUu52PhoFmzZpRXFx8QhhwOp1VywRRUVF4eXmRkZFRdXt5eTlOp/OUn8Cjo6NZtWpVHb1bkYbVLSCAmV27Er91K79kZeHl7U10y5Z18twlDge5djvn+vuT0rUrbX196+R564NLh4GJ+/aRevgwQVYrQXX06aGsvJxDGRmEhobSMiSEYoeDFTk5BHp48GrHjloukDp37Hp5dXatn2wd/Uzr5TfddFO1wkBpaekJYcB6zLns7dq1IyoqivXr19O/f3+g8gyOHTt2cOuttx73fCJNxQWBgVz10Ufs8vfH48ILyXE4aHaavWhn4jQMsioqMICbwsOZ2KED0T4+dVt0HXPZMLAhL4/kjAz86jAIOJ1ODh44gI+PDy1atAAgwMMDp2GwMCuLa0JD6d2sWZ28ljQdFRUVZ9VYpaSk5JTP7efnd8JaeLNmzWjfvv0Zry2v6Xp5s2bNqKioOG7PQHl5OevWrcNisdCzZ0/atGnDkCFDiI+P5+qrr+Zvf/sb//3vf9m/fz/x8fGAwoA0PStWrODTBQuY/swzHO7Uif/s389Bmw1fq5VQT89qdxescDo54nBgczqJ9PbmibZtGRgVVXWVmytzyTBQYLfz1O7dlDudtKzD62/T09OxOxy0b9v2uB9oQZ6eFNlsPLdnD5cHB9NcG5KaDMMwKC8vr/FlaMfeVl5efsrnP9nmtlatWp32HPZj/9wQ6+U5OTn8+OOPbNmyhbKyMv773/8SERHBFVdcwd///nfGjx+Pn58fPXv2xGKx0L9/f/Lz83nyySfJyMjg/PPPZ86cObRp06beaxVpaMe2q+/7e7v6v4eFMffwYRZmZZFZUYHTMPC2WvGzWPC2Wjm6oOwEyp1OypxObIaB1WKhpbc3MVFRDIyKoo0LLwv8kcU41ZFYxygoKCAkJIT8/HyCg4PrvagX9+zhzYMHae7tjVcdJaqc3FwyDx+mdZs2BJ1kOtVhGGTYbAxq3pzXO3Wqk9eUs+d0OqvWy2vbKe1Uu+etVutprx8/02ExAQEBjeL68hUrVnDHHXcQGRlZdc10SEgIsbGxxMfHs3z5cvz8/LjpppuqHuN0OikoKMDPzw8fF5/eFKmtwsJC4uLiCAgIICUl5YSZtgK7ndU5OXxRUMB3hYVk2GyU/95xECrPK/CxWGjt60v3wECuCgnhlvBw/F1ok2B1x2+XCwP5djt//f57Cu12mtVgVsBZUUH24sXkf/YZjuJifNq2JapfPwL+9CdKSkvZu3cvEeHhRJ3mCMg8ux2rxcLHF13k8us7jYXD4TjuPPaanv5WnfXyM3VEO9Vgr+vLRdyXYRg8+uijfPvtt6SmplbrcKEsm4195eWU//4zyddq5RxfX8Jd+ATJ6o7fLrdMsCI7m+yKCprX8C83Y+pUCr7+mvCbb8a7eXPyP/2Ufa++SuvHH+eQpyf+fn6nDQIAwR4eZNhsLM7K4p9ufg77UUePcK1tp7QzrZf/ceCOjIykQ4cO1TosRteXi0htzZkzh48//rhG7eojvb2JbKI/d1wuDMw9fBgrVHvDBkDpzp0UfPklUQMGEHHbbQCE/O1v7Bozhr0pKXgnJlbrm221WPC0WJibmcnIVq1cqqNUbRy7Xl7bTmmnuibdYrFUncd+7IDeqlWrak27BwQE6PpyETGF2tWfyKV+GmfZbPxWWkpADddhC7/+GiwWQq+7ruprVi8vrBdfTMXy5bT186v2wBPk4cGh8nJ2lZbS+ZhT2MzgdDopKSk57Vr5mdbRT3b0LBy/Xn7s4B0ZGVmtaXd/f/9GsV4uInKsnJwctas/CZcKA1tLSihzOomo4SfGsj178G7ZEo9jjo4sKiqiJCQET09PLJmZUM1pf1+rlSN2O1tLSs46DDgcjlN+Gq/OgF5UVHTKlqfe3t4nDNghISG0bt36tFPrR7/m6+ur9XIRcSsOh4MxY8aoXf1JuFQY2FZSgoOaLREA2PPz8QwNrfpzRUUFB9PTCWzRglJPT+x5edV+LuvvB01sKynhlj+sl9e0U9rp1sv9/f1P+LQdFRVFx44dq7WjXevlIiI1o3b1p+ZSYSDj9/XpGndEs9mqlgEMw+DAgQN4eHjQPDqa3YaBraSE0tJSHE4nTocDh8Px///9x98dDkoCAvj3Rx+xYOnSk76exWI5brA++nvr1q2rtaM9MDBQiVREpAGpXf3puVQYKHc64cxXOp7A6u2N8fu15KWlpRzJy8Pby4tdBw5gKysjMzeX3GM7rlkseFitWD088LBa8fDwwOrhgZenJ1YPD5ze3px/8cU8eNFFJ93RrvVyEZHG4+DBg2pXfwYuFQasALVYx/YMCaHi96UAbx8f/Pz8Ktu2lpTg6eVFy86dCerYsSoAnOloyIrycjq3bcvtXbrU/E2IiIjLsNlsPPbYY4SqXf1pudTHW38PD2rzbfJp1w5bRgaO0lI8PTzo1rUr7c85B9LTsVdUUOjvj72iAk9Pz2qfER2kaXwRkUbv5ZdfZvfu3bz88ssEBQWZXY7Lcqkw0MnPD+OYox6rK/gvfwHDIO+jj6q+5u/jg9e2bQSeey6EhLBv3z727NlDcXHxaZ/LMAwsFgudjrkyQUREGp+VK1eybNkynnjiCbpopve0XGqZoKu/P95WK+WGgW8NpnL8OnUi6C9/IXPBAhwFBXhFRZH/6afYsrJo98QT+J9zDkXFxWRnZbFv3z78/PxoFhlJYEDACc9lNww8fq9FREQap+3btzNhwgT69OlD7969zS7H5blUb4Iyh4OLv/2Wcoejxmc9OysqyFq0iIJNmyp7E7RpQ+RddxF44YXH3a+4uJisrCxKS0vx9fMjslmz4/rAF9jtOIHPu3dvssdOiog0ZUVFRcTFxeHn50dKSopbN9tqlL0JfD08uCE0lEVZWYT9Pl1fXVYvL5oPHEjzgQNPe7+AgAACAgIqQ0F2Nvv378fX15fIyEgCAwMpdDq5NiREQUBEpBEyDIPnnnuOI0eO8Oabb7p1EKgJlwoDAAOioliRk0OZ04lfPW7iqwoFJSVkZ2Wxf/9+vP398YiIYFDz5vX2uiIiUn/S0tJYv349r732Gq3VcK7aXGoDIcCVISF09ffnyCnO1K9rAf7+tGvXjnbt2mHz9aV0715SR49m/fr1p2ydKyIiruf777/njTfeYMiQIVxzzTVml9OouFwYsFos/LNVKzwtFooaKBAA4ONDaFgYj597LuFBQTz22GMMGjSIDz74QKFARMTFHW1AdNFFF3HfffeZXU6j43JhAKBnRAS9IiLIs9tx1OJEwppyGga5djvXhoby1OWXM2XKFKZNm0ZERARPPPEEAwYMYN26dQoFIiIuyOFw8K9//Qun06kGRLXkkmHAYrEw7pxzaOPjw6GKihqfO1AThmFwuKKCSG9vXmjfvupQoosuuojJkyczY8YMmjdvzpNPPklMTAxr165VKBARcSFvv/023333HRMnTqRZs2Zml9MouWQYAIjy9uaNzp0J8/Qko54CwdEg4O/hwWsdO9L+JAcNXXjhhbz55pukpKTQsmVLnnrqKe6++27WrFmDoyGXMURE5AQbNmwgJSWFUaNG0b17d7PLabRc6pyBk/n4yBHu++03cisqaO7tXeP2xqfiMAwOVVQQ5OHBqx070ruaaXLLli0kJyezceNG2rZty4gRI7jllls0LSUi0sAOHjxIbGws3bt359VXX1XfgZOo7vjt8mEA4KuCAh7csYOdpaUEe3gQ5OFxVt/0IoeDPLud1j4+vNyxI9eHhdX4ObZu3UpycjIbNmygTZs2xMfHc+utt1a1UhYRkfpjs9mIj4+nsLCQ1NRU9R04hSYVBgCOVFQwcd8+3snMpMzpJMTTk0CrtdqhwDAMSpxO8hwOvCwWekVEMO6cc4g6y8OFfv31V5KTk/n4449p1aoV8fHx3H777QoFIiL1aPz48axatYqUlBTOPfdcs8txWU0uDBz10ZEjJGdk8EVBASUOB94WC75WK35WK54WS1U4MAwDu2FQZhiUOZ2UO534Wq10DwoioWVLbg0Pr9Mppe3btzNt2jTWr19PdHQ0w4cPp2fPnnjV8FhlERE5vdWrVzNu3DjGjh1Lnz59zC7HpTXZMHDUluJiFmZm8kFeHhnl5ZQ5nTigqgWyYRhYLRb8rFaivL25NjSU/pGR/DkwsF7XlXbs2MG0adP48MMPad68OcOHD6dXr15463hjEZGztmPHDoYOHcrNN9/M008/bXY5Lq/Jh4FjZdtsbC0pYU9ZGWVOJwbga7XS1seHbgEBRHl5NfjGkl27djFt2jTef/99oqKiGDZsGH369FEoEBGppaKiIoYMGYKvr6/bNyCqLrcKA65s165dzJgxg3Xr1tGsWTOGDRtG3759FQpERGrAMAwef/xxvvzyS1JTU2nTpo3ZJTUK1R2/XfacgaaiQ4cOvPDCCyxcuJBLL72UV199ld69ezNv3jzKy8vNLk9EpFGYN28e69ev55lnnlEQqAeaGWhg+/btY/r06bz77ruEhYUxdOhQ7rzzTnx9fc0uTUTEJf34448kJSUxaNAgHnjgAbPLaVS0TODi9u/fT0pKCqtWrSI0NJQhQ4Zw11134XeSUxBFRNxVbm4ugwcPpnXr1rz99ts64K2GFAYaiYMHDzJjxgxWrVpFcHAwcXFx9OvXD39/f7NLExExldPp5L777mPnzp3MnTtXfQdqQWGgkUlPTyclJYUVK1YQFBREbGws/fv3VygQEbf11ltvMXPmTKZMmcIll1xidjmNksJAI5WRkcHMmTNZvnw5/v7+xMbGEhMTQ0BAgNmliYg0mE8//ZTRo0czatQohg0bZnY5jZbCQCN3+PBhZs6cybJly/Dz82PQoEEMGDCAwMBAs0sTEalX6enpxMbGctFFF/Hqq69iterCt9pSGGgiMjMzq0KBj48PAwcOZODAgWrKISJNks1mY8SIEeTn55Oamqox5ywpDDQxWVlZzJ49m8WLF+Pt7V0VCvT9EJGm5GgDohkzZtC1a1ezy2n0dOhQExMZGcnDDz/MihUr6N27N7Nnz6Znz5689dZb5Ofnm12eiMhZW7NmDUuWLOHRRx9VEGhgmhlopHJzc5k9ezaLFi3CarUSExPD4MGDCQ0NNbs0EZEaO9qA6MYbb2TcuHEN3k+mqdIygZvIzc0lNTWVhQsXAtC/f39iY2MJCwszuTIRkeopLi4mLi4OHx8fUlJSdCJrHVIYcDNHjhwhLS2NBQsW4HQ6ufvuu4mLiyM8PNzs0kRETskwDJ544gm++OIL5syZQ9u2bc0uqUnRngE3ExYWxqhRo1i5ciWDBg1i8eLF9OrVi3//+9/k5OSYXZ6IyEnNmzePDz/8kHHjxikImEgzA01UQUEBc+fOZd68eVRUVNCvXz+GDBmi4zxFxGUcbUA0YMAAHnzwQbPLaZK0TCBA5fdu/vz5zJ07F5vNxp133smQIUOIiooyuzQRcWO5ubnExsYSHR3N22+/jaenp9klNUkKA3KcwsLCqlBQXl5O3759GTZsmEKBiDQ4p9PJqFGj2LFjB2lpaURGRppdUpOlMCAnVVRUxDvvvENaWhqlpaX06dOHYcOG0aJFC7NLExE3MWXKFFJSUnjrrbe49NJLzS6nSVMYkNMqLi5mwYIFpKamUlxcTO/evRk+fDgtW7Y0uzQRacI+++wzHnjgAe677z6GDx9udjlNnsKAVEtJSQkLFy5kzpw5FBYW0qtXL+Lj44mOjja7NBFpYo42IPrzn//Ma6+9pgZEDUBhQGqktLSURYsWMWfOHPLz87n99tsZMWIErVq1Mrs0EWkCbDYbCQkJ5OXlqQFRA1IYkFopKytj8eLFzJo1i7y8PG6//Xbi4+Np06aN2aWJSCM2ceJEli9fzowZM+jWrZvZ5bgNHTokteLr68vgwYNZsWIFo0ePZtOmTdx11108/fTT7Nu3z+zyRKQRevfdd1m0aBGPPvqogoCL0syAnFZ5eTnLli1j5syZ5OTkcPPNNzNixAjOOeccs0sTkUZg165dDBkyhBtuuIFnnnlGDYgamJYJpE7ZbLaqUJCVlcWNN95IQkICHTp0MLs0EXFRJSUlxMXF4enpyaxZs9SAyARaJpA65e3tTf/+/Vm2bBmPP/44P/74IzExMYwZM4adO3eaXZ6IuBjDMDh8+DAOh4NXXnlFQcDFaWZAasVms7Fq1SpSUlLIyMjghhtuICEhgc6dO5tdmoi4CLvdjtPpxNvb2+xS3JaWCaRBVFRUsHr1ambMmEF6ejrXXXcdiYmJdOnSxezSRETcnsKANCi73c6aNWuYPn06Bw8e5JprriExMZGuXbuaXZqIiNtSGBBT2O121q5dy/Tp09m/fz9XX301CQkJnHfeeWaXJiL1yDAMXSngghQGxFQOh4P33nuPadOmsW/fPq666iqSkpI4//zzzS5NRM7SkSNH2Lp1K6WlpQQEBHDFFVcAld0IdcSwa1EYEJfgdDqrQsHevXu58sorSUxM5IILLjC7NBGpBafTyRVXXEFxcTEHDhwgOjqa9u3bM2vWLCIjIzVD4GJ0aaG4BKvVyq233srChQt58cUXycjIYPjw4YwaNYqffvrJ7PJEpIYGDhyIn58fa9as4YsvvmDs2LHk5OTQuXNn3nvvPQWBRkphQBqE1Wrl5ptv5p133mHChAlkZmYSHx/PyJEj+f77780uT0SqobCwkMzMTJKSkmjXrh3dunUjJiaGBQsWcPfdd9O3b19SUlLMLlNqQWFAGpTVauXGG29k/vz5vPTSS+Tm5pKYmMi9997Ld999Z3Z5InIanp6eZGdns379+qqveXh40K5dO55//nnuueceZs2axaFDh0ysUmpDYUBMYbVaueGGG5g7dy4vv/wyBQUFJCUlkZSUxDfffEM1trKISAPz8/Pj4Ycf5ssvv2Tu3LnY7faq21q0aEFcXBybNm3iyy+/NLFKqQ2FATGV1Wrl+uuvJy0tjddee43i4mLuvfdekpKS+OqrrxQKRFzMddddR6dOnZg8eTKpqalkZ2dX3da9e3d69OhBUVGRiRVKbehqAnEphmGwceNGpk2bxpYtW7jwwgtJSkri8ssv18YkEReRk5NDYmIimzdv5rbbbqNnz55ccMEFLF++nPvvv5+ffvpJR5O7CF1aKI2aYRhs2rSJqVOnsnnzZi644AISExPp0aOHQoGIiRwOBx4eHgC88sorrF69ml9//ZWKigoiIyN59NFHiY+PN7lKOUphQJoEwzD4/PPPSU5O5ueff+b8888nMTGRq666SqFAxCTHBoLdu3djs9nYu3cvXbp04ZxzzjG3ODmOwoA0KYZh8OWXXzJ16lR++uknzjvvPBISEvjb3/6mUCBSD44cOUJYWNhJb9PBQo2HDh2SJsVisXDFFVcwffp03nrrLXx8fHjooYeIi4vjk08+qfZGQ4fDwS+//EJBQUE9VyzSeD3yyCM8+uij7Ny586S3Hw0CGzZsYMeOHQ1ZmtQTzQxIo2QYBt9++y1Tp07lu+++o0uXLiQkJHDttdee9mz0LVu28PLLL/Pdd9/Ro0cPXnrpJUJDQxuucBEXt3DhQgYNGoTVauXSSy/l5Zdf5rLLLsPLy+u4+x06dIghQ4aQm5vL2rVradasmUkVy+loZkCaNIvFwqWXXsrUqVOZOnUqwcHBPPbYY3z88cc4nc5TPq5ly5bExMRw//33k5yczNSpU6moqGjAykVcV1FREcuXL+fxxx9n27ZtVFRU0LdvX+bOnXvCbFqLFi1ITEzkgQceUBBoAjzNLkDkbHXv3p23336bzZs3061bt9PODISFhXHrrbfy/PPP07ZtW2677bYTPvGIuCtvb29uu+02goKCaN++PV999RX33Xcfw4cP5+eff2b06NG0bt2a8vJyli5dSt++ffH19TW7bKkDWiYQt3HspqeQkBAeeOABnnrqKby9vU2uTMR1HHulwFFz585l+PDhXHvttUyYMIHU1FSWLVvG5s2b8fPzM6lSqY7qjt+aGRC34XA48PT0ZOLEifj6+hITE6MgIPIHxwYBp9OJ1Wpl0KBBXHHFFfTq1YvrrruOwsJC1q1bpyDQhGjPgLgNT8/K7Dtx4kSSkpLo1KnTGR+zbds23nnnHXbv3l3f5Ym4nKNLbg6Hgw4dOrB06VIKCwt54403+Pvf/25ydVKXFAbELRzdVPjyyy/j7e1N//798fHxOeX99u3bx9ixY7nkkkt4+eWX6datG4mJiRQXFzdo3SKuwMPDg7y8PPr168fVV1/NqFGjzC5J6pjCgDRphmHwwQcf8PXXXwPw0ksvkZiYSJcuXU56/6OfhJ599lmWL1/Om2++ySeffMKGDRv45ptvjmvdKtIUnW4bWb9+/fRvoIlSGJAmzTAMPvzwQ3r06EH79u2pqKggJibmhFkBwzBwOBwALF68mE8//ZSkpCTi4+MJDAzksssuw26388knn5jxNkQaRElJCZs2bTrpbaGhoYwdO/a0V+tI46XvqjRpVquVCRMmsG3bNq6//nqKiop44IEH+OabbwCq+rFbLJaqjVNvvvkmF198Mb169ap6nsLCQrp166aTC6XJMgyDF198kTFjxpCTk3PS++gI4qZLYUDcQpcuXZg+fTo7duwgPDycSZMmATBlyhQmTZpESUkJABs3biQ3N5ebbrqJdu3aVT0+IyODL7/8km7dugGnn0oVaYwWLlzIe++9x1NPPUVERITZ5UgD06WF4lY6dOjA4sWLKSsrIz8/nwULFuDl5VW1ISojIwPDMOjevXvVYxwOBytXriQrK4t//OMfgD4hSdPyyy+/8PrrrxMTE8NNN91kdjliAoUBcUu+vr74+vqyZMkSMjIyjvv69u3bOffcc6u+9uOPP/Lmm28yatQofH19q669FmkK8vPzefzxx+nWrRujR482uxwxiX6iiVuLjIzkwgsvrPrzBRdcwIUXXsiSJUsA2LRpExMnTsTLy4vx48cDKAhIk+F0Ohk7dizl5eVV/5+Le9JPNZFjtGvXjt69e5OQkMBVV11Fr169KC4uZsqUKXh6ep62CZJIYzN9+nQ+//xzXnzxRZo3b252OWIi9SYQOYlDhw6xaNEiOnXqxFVXXUVQUNAJ93E6nRw+fJgPPviAfv366WhWaVS++OIL/vnPf5KUlERiYqLZ5Ug9qe74rTAgUg3HNjk61vLlyxk/fjxBQUHExcVx99134+/vb0KFItV3+PBhBg0axHnnncd///tfLX01YQoDIg0kPT2dmTNnsmLFCgICAoiNjSUmJkahQFxSRUUFSUlJZGZmkpaWRmhoqNklST1SGBBpYIcOHWLmzJksX74cPz8/Bg8eTExMDIGBgWaXJlLl1VdfZdGiRUyfPp3zzz/f7HKknlV3/NbckEgdadGiBU888QTLly/nlltuYdq0afTq1Yvk5GQKCwvNLk+EdevWMX/+fB566CEFATmOZgZE6klmZiazZ89myZIleHt7M2jQIAYMGKB/Q2KKPXv2MGTIEP72t7/xwgsv6OAsN6FlAhEXkZ2dzezZs1m0aBFeXl4MGDCAwYMH69+SNJiSkhKGDRuGYRjMmjVL+1nciMKAiIvJycmpCgUeHh5VoSAkJMTs0qQJMwyDp59+mo8//pjZs2fTvn17s0uSBqQ9AyIuJiIiggcffJCVK1fSr18/5s6dS69evZg0aRJ5eXlmlydN1OLFi3n33XcZO3asgoCckmYGRExy5MgRUlNTWbBgAQB33303sbGxhIeHm1yZNBVbtmxhxIgR3HHHHTz22GNmlyMm0DKBSCORl5dHWloa77zzDk6nk379+jFkyBCFAjkr+fn5DB48mIiICJKTk/H29ja7JDGBwoBII1NQUEBaWhrz5s3D4XBw1113MXToUPWWlxpzOp2MHj2azZs3k5aWRosWLcwuSUyiPQMijUxwcDD/+Mc/WLVqFUOGDGHFihX06tWL1157jaysLLPLk0YkJSWFzz//nBdeeEFBQKpFMwMiLqqwsJB58+Yxb948ysvL6du3L8OGDSMqKsrs0sSFffXVV9x3330kJiaSlJRkdjliMi0TiDQRRUVFzJ8/n7lz51JaWloVCtRyVv4oMzOTQYMG0bVrV9544w01IBKFAZGmpri4mHfeeYfU1FRKSkro06cPw4YNo2XLlmaXJi6goqKCe+65h0OHDjF37lw1IBJAYUCkySopKWHBggXMmTOH4uJievXqxfDhw4mOjja7NDHRa6+9xsKFC0lOTuaCCy4wuxxxEdpAKNJE+fv7M2zYMFauXMnIkSP5+OOPueOOO3j++ec5ePCg2eWJCT744APmzZvHgw8+qCAgtaKZAZFGrrS0lMWLFzN79mzy8vLo2bMnw4cPp02bNmaXJg1g7969xMXF8de//pUXX3xRDYjkOFomEHEzZWVlLFmyhFmzZnHkyBFuvfVWRowYQdu2bc0uTepJaWkpQ4cOxel0Mnv2bDUgkhMoDIi4qfLycpYuXcrMmTPJzc3llltuIT4+nnPOOcfs0qQOGYbBuHHjWL9+PbNnz6ZDhw5mlyQuSHsGRNyUj48PAwYMYMWKFTzyyCN888033H333fzrX/9i9+7dZpcndWTJkiWsWbOGp556SkFAzppmBkSaOJvNxvLly0lJSSErK4sbb7yRhIQEDSCN2NEGRH379uXxxx83uxxxYVomEJHj2Gw2Vq1axYwZMzh8+DA33HADCQkJdOrUyezSpAYKCgoYPHgw4eHhakAkZ6QwICInVVFRwapVq0hJSSE9PZ3rr7+ehIQEunTpYnZpcgZOp5MHH3yQn3/+mbS0NB04JWekPQMiclJeXl7ccccdLFmyhLFjx7Jt2zYGDRrEI488wq+//mp2eXIaM2fOZNOmTbzwwgsKAlKnFAZE3JSnpyd9+vRhyZIljBs3jh07djB48GAeeughtm7danZ58gdfffUVb7/9NiNGjODKK680uxxpYrRMICIAOBwO1q5dy/Tp09m3bx9//etfSUpK4rzzzjO7NLeXmZnJ4MGD6dKlC2+++aYaEEm1ac+AiNSKw+HgvffeY/r06ezdu5crr7ySpKQk/vSnP5ldmluy2+3cc889ZGRkkJaWRlhYmNklSSOiPQMiUiseHh7cdtttLFy4kBdeeIH09HSGDRvGP//5T3766Sezy3M7b7zxBr/88gsTJ05UEJB6ozAgIidltVq55ZZbWLBgAePHj+fw4cPEx8czcuRIfvjhB7PLcwsffvghc+fOZfTo0Vx44YVmlyNNmJYJRKRanE4n69evJzk5mZ07d/KXv/yFxMREunfvbnZpTdK+ffuIjY3lqquuYvz48WpAJLWiPQMiUi+cTicff/wxycnJ/Pbbb1xyySUkJSVxySWXmF1ak1FWVsbQoUOx2+3MmTNHDYik1rRnQETqhdVq5frrryctLY1XX32VoqIi7rnnHpKSkvjqq6+oxucLOQ3DMJgwYQIHDx7klVdeURCQBqEwICK1YrVaufbaa0lNTeX111+ntLSUkSNHkpCQwJdffqlQUEtLly5l9erV/Otf/1L/CGkwCgMiclYsFgtXX301s2fP5j//+Q8VFRXcd999jBgxgs8//1yhoAa2bt3KK6+8wl133cWtt95qdjniRrRnQETqlGEYfP7550ydOpVffvmFP/3pTyQmJnLllVdqE9xpFBQUEBsbS2hoKNOmTVMDIqkT2jMgIqawWCxceeWVpKSkVJ2W98ADDzB06FA2btyomYKTcDqdjBs3jqKiIiZOnKggIA1OYUBE6oXFYqFHjx5Mnz6dt956C29vbx588EGGDBnChg0bFAqOMWvWLDZu3Mjzzz9PdHS02eWIG1IYEJF6ZbFYuOyyy0hOTmbKlCn4+fnx0EMPMXjwYD766COcTqfZJZrqm2++YcqUKcTHx3PVVVeZXY64Ke0ZEJEG9+233zJ16lS+/fZbOnfuTGJiItdee63bNeDJzMwkNjaWTp06MWnSJLd7/1L/dOiQiLi87777juTkZL7++ms6duxIYmIi119/vVsMisc2IEpNTSU8PNzskqQJ0gZCEXF53bt3Z8qUKUybNo1mzZrxxBNPMGDAANatW9fklw8mTZpU1YBIQUDMpjAgIqa76KKLmDx5MjNmzKB58+Y8+eSTxMTEsHbt2iYZCtavX09qaioPPPCAGhCJS9AygYi4nF9++YXk5GQ+++wz2rVrx4gRI7j55pvx8PAwu7Sztm/fPuLi4rjiiiuYOHGizl6QeqU9AyLS6G3ZsoXk5GQ2btxI27ZtGTFiBLfcckujDQVlZWUMGzYMm83GnDlzCAgIMLskaeIUBkSkydi6dSvJycls2LCB1q1bEx8fz2233Yanp2fDFFBRAb/9Blu3Vv7avx9KSsDDAwIDoVMn6Nat8lebNnCST/uGYfDss8/y/vvvM2vWLDp16tQwtYtbUxgQkSbn119/Zdq0aXz00UdER0czYsQIbr/99voLBfv3w+LFMG8eHDoE5eVgtYJh/P+A73RWfg3A1xfOPx8GD4bbb4egoKqnWrZsGS+88ALPPfcct912W/3UK/IHCgMi0mRt376dadOmsX79elq2bMnw4cPp1asXXl5edfMChw/DhAmwahUUFYGXFwQHg4/P/w/8f+RwQGkpFBRU/jkqCpKSICGBbbt2ER8fT8+ePXnyySfrpkaRalAYEJEmb8eOHUybNo0PP/yQ5s2bV4WCWp/tbxiwYgU8/zwcOFC5BBAcfOoAcCp2O+TmQkUFFd27M7qkhPxWrZgxY4b6DkiDUhgQEbexa9cupk2bxvvvv09kZCTDhw+nT58+NRt4bTZ46qnKJQGns/KT/VluVDTKyynctYs8wyDw9dcJv/fes3o+kZpSGBARt7N7926mT5/OunXraNasGUOHDuWOO+44cyiw2eDBB2HJksp1/jr6OZedk0Pm4cN0CAzENyAAnn0Whgypk+cWqQ6FARFxW3v37mX69OmsXbuW8PBwhg4dyp133omPj8+JdzYMeOwxSE2F0FCoo8v9iktK2Ld3LxHNmhHVrBlkZYGnJ7z+OvTtWyevIXImCgMi4vb27dvHjBkzWLNmDWFhYVWhwNfX9//vtHgxjB4N/v7H7f4/GxV2O7t37cLH15e2bdtigcrQcegQhIfDypVwzjl18loip6MwICLyu/3795OSksKqVasIDQ0lLi6Ofv364XfkCPTsCZmZ0KJFnbyWYRjs3bcPm81Gh/btj7/s0emE9HS48UaYNavmGxNFakhhQETkDw4ePMiMGTNYtWoVwcHBvOl00uWXX7BGR5/1ZsGjDmdmkpuTQ7t27fD39z/xDiUlkJ8Pr70GAwbUyWuKnIq6FoqI/EGrVq0YO3YsS5cu5c4LLyTos884WFhIdl4ejjpoiFRQWEhOTg5RzZufPAhA5XKEYcC0aZUzBSIuoIHO8hQRcR3R0dH8IzISR3AwmV5eZGVlkZOTQ0R4OGHh4XicYvq+xOlkTk4Ov5SWsrmsjAKHg3EtW9IrNBSbzUZ6ejrBQUFnbkkcFga//gqffgpXX10P71CkZjQzICLup6QEFizAw8+Pli1b0qljR4KDg8nKzmbHjh1kZWefdKYgz24nOTub3TYbnY+5MsFpGBw4eBBPT09aRkdzxj6Efn6V/Q7eeadu35dILWlmQETczy+/VF7qFxoKgJeXFy1btKBZs2bkZGeTnZ1Nbk4O4eHhhEdEVM0UNPP05L3OnYnw9GRLaSlD9uwB4NChQ9jKyzmnfftTziqcwM+vcmbA4aiz/QoitaWZARFxP9u2VX4y/8NhRF6enrRo0YJOnToREhpKdk4Ov/32G5lZWTgcDrytViL+0BSpqLiYvLw8WrRsie/JzjE4FT+/yj4GvwcKETMpDIiI+9mypbLr4ElaDcPvoaB5czp16kRYaCi5OTn8tmMHmVlZ2B2OqvsZTidHjhwhLDSU0JCQmtXg4wNlZZUtkUVMpmUCEXE/O3dW6xp/L09PmjdvTkREBDm5ueTm5JCbm0t4WBgVvr7YKirw8vOjeW3OKPDwqAwj+/bV4g2I1C2FARFxP6WlNTrwx9PTk+ZRUUSEh1eGgiNH2GOzYRgGEc2aYT3FDMMZWSxQXl67x4rUIYUBEXE/tRy8q0JBRASFR47gk5mJ19ls/jMMnUIoLkH/F4qI+/H3r9zFX0ueHh4EBARgqe2MwLGO7ZMgYhKFARFxP+eeW/mp3Ex2e+Xv7dubW4cICgMi4o66dasMA2YGgrKyylmBrl3Nq0Hkd9ozICLu57zz/v/SPj+/Gj10QW4uhU4nWb9/st9YVETm7/8dExZGYHX3EJSVQbNm0Lp1jV5fpD4oDIiI++nWrXIQ3ru3xmFgTm4uGRUVVX9eX1jI+sJCAG4NDq5eGDCMyjBw443aQCguQWFARNyPtzcMGgTPP1/ZObAGA/LKTp3O/vVLSiqXCPr3P/vnEqkDiqQi4p7uvBOCgyE/v+FfOy8PLrqo8peIC1AYEBH31LIlxMRAcXFln4KGUlhYOTPxj3/U+rwDkbqmMCAi7uuRR6Bz58oOhg1xZYHdXjkTceedcNNN9f96ItWkMCAi7iskBF54ofLKgiNH6ve1DAMOH4ZzzoF//UuzAuJSFAZExL1dcw2MHg02W+Vafn0wDMjIgNBQeP31yksKRVyIwoCIyP33w8iRlZf7ZWfX7ZKBwwHp6ZWzEJMmwZVX1t1zi9QRhQEREYsFxoyBJ58ET084eLBypuBsFRVVzgi0bg3TpsENN5z9c4rUA4UBERGoDAQjR8KCBZWX/GVlQWZmza80MIzKcwQOHqz8/a67YPVquOqqeilbpC7o0CERkWN17w5Ll0JyMsyeXTnFbxgQGFh5UJC394mb/5zOyiWGsrLKAODtDX/+M9x3H/Tsqc2C4vIshnHmxbGCggJCQkLIz88nODi4IeoSETFfSQmsXQvz5sGPP1YO9hUVJw8Dvr4QEFB5xHBMDFx+uY4aFtNVd/zWzICIyKn4+1eeCXDnnZWHBW3bBlu3Vs4WlJdXDvZ+ftChQ2W/g44dK2cFRBoZhQERkeoICoK//KXyl0gTozksERERN6cwICIi4uYUBkRERNycwoCIiIibUxgQERFxcwoDIiIibk5hQERExM0pDIiIiLg5hQERERE3pzAgIiLi5hQGRERE3JzCgIiIiJurVqOio12OCwoK6rUYERERqTtHx+2j4/ipVCsMFBYWAtCmTZuzLEtEREQaWmFhISEhIae83WKcKS4ATqeT9PR0goKCsFgsdVqgiIiI1A/DMCgsLCQ6Ohqr9dQ7A6oVBkRERKTp0gZCERERN6cwICIi4uYUBkRERNycwoCIiIibUxgQERFxcwoDIiIibk5hQERExM39H/nlCZH5SNDXAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -507,7 +504,7 @@
     }
    ],
    "source": [
-    "# create minimum eigen optimizer based on VQE\n",
+    "# create minimum eigen optimizer based on SamplingVQE\n",
     "vqe_optimizer = MinimumEigenOptimizer(vqe)\n",
     "\n",
     "# solve quadratic program\n",
@@ -569,9 +566,9 @@
     },
     {
      "data": {
-      "image/png": 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mWUXodujQgR9++IG0tDR+//13fv31V7y8vADo1q0b7dq1U7lC+9WoUSMmTZrEG2+8wTvvvMMrr7wiDXSEuAVWEbo9e/YkLCyMKVOm8PrrrxMWFsaIESMwmUzcddddErpWoGPHjiQkJBAYGEhkZCSffvqpbGgR4iYoVf3B6dy5s8lSJxEcPHiQI0eOEBgYSIcOHXB3dwcgPz+f6Oho/v3vf+Pg4GCRWkTVfvvtN2bOnIm3tzeTJ0/Gz89P7ZKEsCqKouw3mUydK7pmFSNdgNjYWB5++GG6d++Ou7s7s2fPJiMjA1dXV9LS0vjjjz/ULlH8T5s2bYiPj6d9+/b07duX5ORkaaAjRA1ZTeg2atSIS5culf/+m2++Yfv27QB4eXlJ6FoZR0dHBg8ezIoVK9i6dSvDhg2TddVC1IDVhG6bNm1Yv349ACkpKRQVFfHJJ5/w6quv0qpVK2677TaVKxQVCQkJYcWKFfTo0YPBgwezevVqSktL1S5LCKtlNaEbHR1NVlYWvXv3Zvjw4YwfP56pU6dSVFREnz59CAsLU7tEUQmNRkNERARr1qzhxx9/JCYmhqNHj6pdlhBWyWoepAFkZmayY8cOQkNDad++vTw4s0Emk4ktW7awePFinnvuOYYOHYqTk5PaZQlhUTbxIA3M87ovvPACnTp1Kg9co9EoD2lsiKIo9O7dm4SEBE6dOkVUVBSHDx9WuywhrIZVhe7y5cvJy8sDKF8DqtFo0Gg0sibUxnh7ezNv3jxiY2OZNGkSb775Jvn5+WqXJYTqrCp0GzduXP4HU1EUTCZTebMbaXJumx555BGSk5PJz88nIiKC3bt3q12SEKqymjldk8lUHqwGg4Evv/wSg8FAaWkpjo6OtGjRgt69e9OoUSOL1CPq3u7du5k1axadOnVi3Lhx0kBHNFg2MaerKAqlpaWsXbuWmJiY8kbaer2e9PR0Nm7cyJtvvqlyleJW3H///SQlJeHq6kp4eLg0MhJ2yWpGugAnT55k1KhRxMbG0qlTJ3x9fcuvpaWl0bVrV06cOGGxekT9OXToEDNnzqRly5ZMnDgRb29vtUsSos7YxEgXzMeEHzx4kF69epUHbk5ODocPH2bhwoU888wzKlco6kr79u1JSEggODiYqKgodu/eXenD0u+++46NGzdauEIh6odVjXQBYmJiuHjxIu3ataNJkybk5uaSmZlJUVER06ZNIyQkxKL1iPr3xx9/4OvrW97k6Kq8vDzee+89kpOTCQ0N5dixY6xfv56WLVuqVKkQNVPVSNcqTo641rx589ixYwfZ2dnk5uYSFBRE9+7d6dGjh9qliXrSunXrCke5J06cYM+ePSxbtoyOHTsybdo0du7cKaErbJrVha6vry9RUVHlGyI0GquaARH1pKIlgX/88Qcmk4mOHTtSVlZGbm4uXl5enDt3juXLl9OlSxeeeuopFaoV4uZZXeheJWErbr/9dgwGA4mJiaSmppKeno6DgwM6nY6goCB69+7Nhg0bePbZZ9UuVYgas8pkMxqNFBcXq12GUFFxcTHt2rXjo48+4quvvmLv3r24u7tz33334e3tjaIohIeH06FDB7VLFaJWrDJ0T506xZYtW9QuQ6hk8+bN9OjRgxMnThAWFkZwcDDNmzfnmWeewdPTk2PHjhEfH8/gwYNp3ry52uUKUStWOb1QWFjIhg0b5Nh1O9W7d29OnTrF448/TseOHXF0dGTy5MnccccdAMTFxdG9e3e6desm28OFzbHK0A0ODiYlJQWj0Shzu3Zq1KhRREVFoSgK3t7e5Y3R161bR1ZWFjExMbi6ulb6/qunkFw9VVoIa2GVoduoUSP0ej1paWkEBASoXY5QiY+PD2A+nHTu3LmkpKRw7tw5XnvttUqnFUwmE2PGjOHs2bN8//33TJ06ldjYWEuWLUSVrHYYGRoaysmTJ9UuQ1gBV1dXRo0axeXLl/nhhx/IycnB0fHP8cLVNb4XLlxgyZIlHD16lFWrVnHw4EG2bNlCSkqKSpULcSOrDt1Tp06pXYawEl5eXnzyyScsX76ct99+m/T09PJriqJgNBrZs2cPhw4dYsqUKej1enJzc8nIyLhhp5sQarLa0A0JCZHQFTeIioriiy++wNfXl4MHD7Jz507A/PD1v//9L23atOHBBx8E4Oeff+b//u//5OQRYVWsOnRlekFU5OpRTiUlJeU/mE+ePMmnn37KwIEDAXPg/vjjj/j4+MjDNGFVrPJBGvwZutc2NxfiWl26dKFLly6AuRvd/fffX75NeMeOHeTk5PDyyy8DyH9HwmpYbei6u7vj7OxMRkbGdX11hahI69atOXjwIH369CEjI4OuXbsSHR1Ns2bNJHCFVbHa0IU/H6ZJ6Irq+Pj4cPjwYVavXo2npydPP/10+TVFUSgqKuLixYsEBgaqWKUQVjynC7JsTNTewIEDywP36lIyk8nEuXPnGDBgAB988AElJSVqlijsnFWHbnBwsKxgEDft6pSCoiiEhoby8ccf88svv9C/f3+OHDmicnXCXll16MpIV9Slpk2bsnDhQmJiYhgzZgyLFi2isLBQ7bKEnbGJ0K3qSCEhakNRFHr16kViYiIXLlwgKiqK/fv3q12WsCNWHbpNmjQB4PLlyypXIhoaT09PZs2axdixY5k2bRqzZs0iNzdX7bKEHbDq0L06FyfzuqK+PPTQQyQlJWEymQgPD+e7775TuyTRwFl16ILsTBP1z83NjSlTpvDPf/6TefPmMXXqVPnblag3NhG6MtIVlnDPPfeQlJSEl5cXERERbNu2TZ4niDpn9aErKxiEJTk7OzN27FgWLFjAhx9+yLhx467raCbErbKJ0JWRrrC0O+64g3Xr1tGmTRuio6PZsGGDdCsTdcLqQ9fHx4eCggJycnLULkXYGa1Wy7Bhw1i6dCmbNm1ixIgRnDlzRu2yhI2z+tBVFEXmdYWqWrVqxerVq3nwwQcZOHAga9eulVGvuGlWH7og87pCfRqNhn79+rF69Wp27tzJwIEDOX78uNplCRtkE6ErI11hLYKCgli6dCnPPfccw4cPZ9myZRQXF6tdlrAhNhG68jBNWBNFUXjuuef4+OOPOXr0KP369eOXX35RuyxhI2widGWDhLBGvr6+zJ8/n6FDhzJu3DgWLFhAQUGB2mUJK2cToevv709WVhZ5eXlqlyLEdRRFoWfPniQnJ5OZmUlkZCQ//vij2mUJK2YToavRaAgODiYlJUXtUoSokIeHB3FxcfzjH/9g+vTpxMXFceXKFbXLElbIJkIXZAWDsA0PPvggycnJODg4EBERwbfffqt2ScLK2EzoygoGYSsaN27Mq6++SlxcHG+//TaTJ08mMzNT7bKElZDQFaKedOzYkYSEBPz8/IiMjOSzzz6TBjrCdkJXpheELXJ2dmbUqFEsXLiQ+Ph4xowZw4ULF9QuS6jIZkK3WbNmZGRkyJlWwia1bduWNWvWcOeddxIdHc369etlK7GdspnQdXBwICgoiNTUVLVLEeKmaLVahg4dygcffMCWLVsYPnw4p0+fVrssYWE2E7ogO9NEwxAaGsrKlSvp3r07gwYNIj4+nrKyMrXLEhZiU6ErO9NEQ6HRaIiOjiY+Pp49e/YQExPDsWPH1C5LWIDNha6MdEVDEhgYyJIlSwgPDyc2Npb3339fGug0cDYVurKCQTREiqLw9NNP8/HHH3P8+HH69u3LTz/9pHZZop7YVOgGBQVhMBgoKSlRuxQh6pyPjw9vvfUWL730EhMmTGD+/Pnk5+erXZaoYzYVuk5OTvj7+8uRKaLBUhSFxx57jOTkZLKzs4mMjOSHH35QuyxRh2wqdEEepgn74O7uzowZM3jllVeYOXMmM2bMkHMCGwibC12Z1xX25IEHHiA5ORlnZ2ciIiL4+uuv1S5J3CKbC11ZwSDsjaurKxMnTmTWrFm88847TJo0iUuXLqldlrhJNhe6skFC2KsOHTqQkJBAUFAQUVFRfPrpp9JAxwbZXOi2aNGC06dPyw4eYZd0Oh0jR45k0aJFrFu3jlGjRmEwGNQuS9SCzYWus7Mz3t7enDt3Tu1ShFBNmzZtiI+Pp0OHDvTr14/k5GRpoGMjbC50QR6mCQHg6OjI4MGD+fDDD9m6dSsvvviiNISyARK6Qti44OBgVqxYQc+ePRk8eDCrVq2itLRU7bJEJWwydENCQuSQSiGuodFoiIiIYM2aNezbt4+YmBiOHj2qdlmiAjYbujLSFeJGAQEBvPvuu0RGRjJy5EiWLFkiDXSsjM2GbkpKijw4EKICiqLQu3dvEhMTSU1NJSoqisOHD6tdlvgfmwzdRo0aodfrSUtLU7sUIayWl5cXb775JrGxsUyaNIk333xTGuhYAZsMXZCHaULU1COPPEJycjIFBQVERESwe/dutUuyazYdurIzTYia0ev1TJ8+nSlTpjBr1iymT58uDXRUYnuhW1QEX35JzwMH6Dx7NnTrBl27Qq9eMHkybNgAmZlqVymEVbrvvvtISkqicePGhIeHs2PHDrVLsjtKVXu3O3fubNq3b58Fy6lCTg4sXw7x8VBYSFFeHpdycwlo3tx8vawMCgvBwcH8+169YPRoaNlSvZqFsGKHDx9mxowZtGzZkokTJ+Lt7a12SQ2Goij7TSZT54qu2cZI99tv4ZFH4P33zb/X63Hw9SXHaMSk04FOB66u4OkJ7u7QuDFs2QJPPmkOalkoLsQN7r77bhISEggODiYqKopPPvlEGuhYgPWH7vvvw+DBkJ8PTZqAkxMAjg4OaBSl4p03Dg7mAHZ2hrlz4cUXzaNgIcR1nJycePnll1myZAlJSUmMHDmS8+fPq11Wg2bdofvhh/DWW+aRq6vrDZd1Oh1FRUWVv1+rBQ8P80h5xAgZ8QpRibCwMOLj4+nSpQv9+/cnMTFR1sHXE+sN3UOHYPZsc+A6Olb4EqfqQhdAUczB+9//wooVdV6mEA2Fg4MDMTExrFy5ku3btzN06FBZIVQPrDN0i4rMD8EcHMyj1UrodDqKqwtdMAevmxssWADHj9dhoUI0PC1atGD58uX06tWLoUOHsnLlSmmgU4esM3Q/+wzOnTMHZRV0Oh1FNd1XrtWaVzgsWlQHBQrRsGk0Gl544QXWrl3LoUOH6N+/P7/99pvaZTUI1he6JhMsXVrplMK1PsnPZ3RWFvf//juv12TyX6+HrVtBzpcSokb8/f1ZtGgR/fv3Z/To0SxevLj6KT1RJesL3TNn4ORJaNSo2pf6Ojnx/7Ra/lbNiLicg4M51L/66haLFMJ+KIrCk08+SWJiIgaDgaioKA4cOKB2WTbL+kL3yBHQaMzzsNV4VK/nIVdXXGuzttBkAmvZ8CGEDfH09GT27NmMGjWKKVOmMGfOHPLy8tQuy+ZYX+j++ivUov+nTqer3SS/szMcPHgThQkhALp3705SUhIlJSWEh4eza9cutUuyKdVPnFrapUvmkW4N6ZydKcjOJr+0lJycHLRaLVqtFgdHRyocKzs4QHZ2nZUrhD3S6/VMmzaNvXv3EhcXR/v27Rk3bhweHh5ql2b1rG+kW4vABfBs0gRXFxeMRiM5OTkY0tI4cfIkR3//neMnTnD69GkMBgMXL14kOyeHgsJCSsrKZOG3EHWgS5cuJCUl4eHhQUREBF9++aVsJa6G9Y10AwLM86614OriQmOtlmYBAeX/rsxopLSkhJKSEor/98/CK1dQCgo4nZXFuK5d8fX1JSAgAH9/f/z8/Mp/7e/vj6+vL441WEEhhL1zcXFh3LhxPPbYY8TFxbF161ZeeeUVfHx81C7NKllfqrRrV95foTplJhNlJhNGwAgUG404KIr5S6PBQadDp9Nd/6ZLlwgcMoT/TpxIWloaBoMBg8HA+fPn2bt3L+fPn8dgMJCZmYmnp2eloezn54dTDesUwh7cddddrF27lpUrVxIVFcXIkSN55plnUGrwUNyeWF9rx4sX4f77zRsjqplqWJ6RwfKLF6/7d8O8vRlW1U/YnBzzBolevaq8d2lpKenp6deFclpaWnkop6eno9frKw1lf39/XFxcavxtC9GQ/PHHH8yYMYPGjRszdepUAgMD1S7Joqpq7Wh9oQsQEwO7dpl7JtSlkhJz05t9++AWA9FoNHLx4sVKQzktLQ1nZ+cqQ9mtpuuLhbBBZWVlrFu3jo8++oghQ4YQGRmJppbPbGyV7YXuzp3mdo56fY3W69ZYZiYMHWo+YaKemUwmLl++XB7IV4P46q8NBgOKotwQxNd+eXh4yF/NhM07ffo0cXFxFBcX89prrxEaGqp2SfXO9kLXZIIBA2D37rob7ebnm5udf/WVuS+vykwmE1euXKkylIuLiysNZH9/f7y8vOxm5CBsm9FoZOPGjbz33ntERkYycOBAtFU0s7J1the6AAYD9OgBRmOFvXRrpbQUrlwxnyLx6KN1U58F5OfnVxnKubm5NG3atNJQ9vX1xeHq8UVCWIELFy4we/ZsDAYD06dPp23btmqXVC9sM3TBPNIdONC8oeFmg/dq4I4cCWPH1ml5aisqKrphHvnaUL58+TLe3t6VhnLTpk1lBYawOJPJxLZt21iwYAFPPvkkw4cPx9nZWe2y6pTthi7A99/DsGHm43bc3Ws3x5ubaw7dMWMgNrZu54dtQElJCenp6ZWGckZGBh4eHlVOYTS0PwzCely+fJl58+Zx5MgRpk2bRqdOndQuqc7YduiCeaphwgTYs+fPhuSVzWWaTOb525ISaNrUvDysc4Xfu90zGo1kZGRcF8TXfqWlpdGoUaPrQjggIAA/P7/yXzeqQTc4SzKZTPLw0cZ8++23zJkzh27dujFq1CgaN26sdkm3zPZDF8xhunOn+cid3bvNUw5FRX92JCsrM2+qMJmgWTN46SV4+ulbXhpmz4xGI5cvX640lA0GA46OjlWGsl6vt3gILlu2jBYtWvD444+jKAplZWUyt23lcnNzWbRoEbt27eLVV1/lwQcfVLukW9IwQvda6enmbmQ//wznz5sD18sL7rgD2raFkBC7m0pQg8lkIicnp8pQLi0trTKUPT096yyUTSYTW7duZeTIkYSEhLB9+3bWrl3L/Pnz0Wq1tGrVig8//FA2rVixH3/8kbi4OO644w7+8Y9/0MQKVhrdjIYXusJm5ObmVhrIBoOBvLy88hCuKJR9fHxqvCzut99+Y8KECXTv3p0zZ84wefJkOnfuzJEjR8jKymLhwoWEhoYSGxt73fuKi4tZsGABycnJtG3blsWLF+Pp6Vkf/3OIGigsLGTp0qV89tlnjB8/np49e9rclFFVoWt9vRdEg9K4cWNat25N69atK7xeUFBwXQ8Mg8HArl27yn+dlZWFj4/PdaHcuXNn7r777uumDA4dOsSKFSsYOHAgeXl5pKenU1paSkhICGVlZQQGBuLk5MS5c+cA89TJ1TD/z3/+w7Zt29iwYQMfffQRb7zxBvPnz5f5YZU4OzszZswYevTowYwZM9i6dSuvvvoqvr6+apdWJyR0hapcXFwICQkhJCSkwuvFxcVcuHDhulDOzMy87jX5+fls3bqVRx99lOeee46ZM2fSqlUrAgMDeeCBB8o3kkRERDBnzpzr3nv06FH27NnDiy++SHBwMF27dmX27Nn19v2KmmvXrh1r165l1apVREdH8/LLL/Pss8/a/IYgCV1h1ZycnAgKCiIoKKjS15w+fZqkpCTKysrYtGkTBw8eJDw8nJkzZ+Lg4EBBQQGff/45mzZtIjU1lVatWpW/NzU1ldLSUu68807AHPJhYWGcOXOmys8UlqHVahk2bBiPPvooM2bMYNu2bUydOtWm/7+x7R8ZQgC33XYbmzdvZvHixbRp04bi4mJSU1NZtmwZ9957LwC9evUiIyOD/fv3A+Y1zAAXL17ExcWlvPlQZmbmDY2IpCm3+lq2bMmqVat46KGHGDhwIGvWrKGsrEztsm6KjHSFzVMUhWbNmtGsWTPatGnD9u3bWbZsGf379yc1NRWAn3/+maysrPJpjKt9lt3c3CgtLS0P1hMnTtCkSRP0en35/QsKCli1ahXHjx+vsGNckyZNZO7XAjQaDX379uXhhx8mLi6OL7/8ktdee+26v7nYAlm9IBoMk8lEeno6mzdvJioqCoPBwJAhQ8jKykKr1TJp0iT69OlDSkoKaWlp3HPPPRQWFvLoo4+yfv16goKC6NixI7Nnz+bxxx+/7r5nz57l5MmT17XxvPrrwsLC64L4r6Hs7e1t8/OQ1sZkMrFp0yaWLFnCCy+8wKBBg6xqS7ssGRN2raSkhIsXL+Lv7w/ATz/9xN69exk0aBAODg7Mnz+fjz76CBcXFx544AHmzp1bqz/A+fn5N5xCcu2W65ycHDkaqp6kp6czZ84czp49y7Rp08rn5tUmoStENc6ePcu5c+e48847cb3VrnZ/UVxcXGUoy9FQt8ZkMvHll1/y1ltv8cQTTzBixIjab4DJzDTveN2/Hw4dMjfJcnCAwEC4917o2BG6dKnxwbkSukJYsWuPhqqoOdFfj4aq6Et22UFWVhbz58/n8OHDTJ06lS5dulT/pmPH4N13YetWcwuBsjJwdv4zXIuLzU2zHB3B0xNefBH69TP35q6ChK4QNuzao6EqCuW/Hg1V0Zc9HQ313XffMXv2bO6//35Gjx5d8fdeUgLLlsHixeag1evNI9uqFBSYux02b25upHXXXZW+VEJXiAZMjoa6UV5eHosXL2bnzp1MmjSJhx9++NqLMGQI/PgjNG4MtT3BIifH/M8334Rnn63wJRK6Qtgxez4a6sCBA8ycOZPbb7+dCRMm4NmokfkosH37zEeB3ewPmuJic3gvWgR/+9sNlyV0hRBVupmjoa5tTmTNR0MVFRWxbNkytmzZwuKAAG7buRPlVgL3zxub53u3bTNPOVxDQlcIcUv+ejTUX78qOhrq2lC2hqOhTmzciOuAAZQ6O+MXEFA3B2NmZcHdd8O//nXdygbpMiaEuCU6nY4WLVrQokWLCq//9Wgog8HAgQMHqjwa6tpQtsTRUC3/9S+MTZtyqbSUk6dO4ePjY95NeCs3dXeHw4fNx4p161ajt0joCiFumVarJTAwkMDAwAqvV3Q01JEjR9ixY4dljoZKSYG9e9Ho9fhoNOjd3Dj/v40r/v7+6K4ZheeUlTHDYGBPbi4ejo6M9PHhCXf3iu97dYpixQoJXSGE9dBoNDRt2pSmTZvSoUOHG64bjUYyMzOvm7I4efLkdb2Vb+loqG3bwGgsnwLQ6XQEBweTmZlJSkoKXl5eeHl5oQBz0tLQKgpfhIVxrLCQ0WfOEObsTGhla3P1evjuO/OGihoszZPQFUKoTqPR4O3tjbe3d4VbeU0mE9nZ2deF8vnz59m/f3+1R0MFBATQ6ptv0Dk6XjeVoABenp64ubmVb9du0rQpX125QnJoKK4aDe1dXXnYzY1Ps7P5e2VN1DUa8+aJ33+He+6p9nuV0BVCWD1FUfDw8MDDw4M2bdpU+JqKjob65ZdfSEtLY+qnn6KUlqLodGi1WrRaLU7/+6dWqyXA35/cvDz2nD6NsaSEZtc8ZGut03EgP7/qAktK4OhRCV0hhP2o8mio227D6OJCSVkZJSUl5V9XcnMpLSmhuKSEsrIyigDH0lKOHj3K7bffjgI01mjIMxqr/vCyMsjOrlGdErpCiIZPUdAoCjqdrryX8l8ZTSZKc3MxnjmDv59f+VREntFIo+o2htSi0b3tbTERQoja8vQ0b2SogkZRaNWoEWg05FzTae5YUVHlD9GucnICH58alSKhK4Ro+O6+29ysphouGg2PuLmxNCODAqORw/n5/PfKFZ6qbMnYVQ4OcPvtNSpFQlcI0fA98IB5yVgNvOLnR5HRSI9jx5h87hyv+vlVPdItKzPfOyysRveXOV0hRMP3xBMwY4Y5IKvpEaF3cGB+bU4bzskxdxur4Y46GekKIRo+Hx94/PE/2zLWFaPRvCstJqbGb5HQFULYh4kTzb1zi4vr7p45OdCrV5UNzf9KQlcIYR+aN4cpU8x9cGs4v1ulvDzztt9//rNWb5PQFULYj7594fnnzS0ZbyV48/LM0worV5qXo9WChK4Qwn5oNDB3LkRFmXeQFRTU7v0mE1y+bJ6mWLsWKmjeU50qm5gripIBpNb6rkIIYd9amEymCndLVBm6Qggh6pZMLwghhAVJ6AohhAVJ6AohhAVJ6AohhAVJ6AohhAX9f51WJasunAACAAAAAElFTkSuQmCC\n",
+      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -583,7 +580,7 @@
     "n = 3\n",
     "num_qubits = n**2\n",
     "tsp = Tsp.create_random_instance(n, seed=123)\n",
-    "adj_matrix = nx.to_numpy_matrix(tsp.graph)\n",
+    "adj_matrix = nx.to_numpy_array(tsp.graph)\n",
     "print(\"distance\\n\", adj_matrix)\n",
     "\n",
     "colors = [\"r\" for node in tsp.graph.nodes]\n",
@@ -613,9 +610,9 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -831,9 +828,9 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -870,9 +867,7 @@
    "outputs": [],
    "source": [
     "algorithm_globals.random_seed = 123\n",
-    "seed = 10598\n",
-    "backend = Aer.get_backend(\"aer_simulator_statevector\")\n",
-    "quantum_instance = QuantumInstance(backend, seed_simulator=seed, seed_transpiler=seed)"
+    "seed = 10598"
    ]
   },
   {
@@ -884,8 +879,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "energy: -7326.024699521826\n",
-      "time: 4.29062819480896\n",
+      "energy: -7326.024699521838\n",
+      "time: 18.355808973312378\n",
       "feasible: True\n",
       "solution: [1, 2, 0]\n",
       "solution objective: 202.0\n"
@@ -893,9 +888,9 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -903,9 +898,9 @@
     }
    ],
    "source": [
-    "spsa = SPSA(maxiter=300)\n",
+    "optimizer = SPSA(maxiter=300)\n",
     "ry = TwoLocal(qubitOp.num_qubits, \"ry\", \"cz\", reps=5, entanglement=\"linear\")\n",
-    "vqe = VQE(ry, optimizer=spsa, quantum_instance=quantum_instance)\n",
+    "vqe = SamplingVQE(sampler=Sampler(), ansatz=ry, optimizer=optimizer)\n",
     "\n",
     "result = vqe.compute_minimum_eigenvalue(qubitOp)\n",
     "\n",
@@ -926,15 +921,17 @@
    "outputs": [],
    "source": [
     "algorithm_globals.random_seed = 123\n",
-    "seed = 10598\n",
-    "backend = Aer.get_backend(\"aer_simulator_statevector\")\n",
-    "quantum_instance = QuantumInstance(backend, seed_simulator=seed, seed_transpiler=seed)"
+    "seed = 10598"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 21,
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "nbsphinx-thumbnail"
+    ]
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -949,9 +946,9 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -959,10 +956,7 @@
     }
    ],
    "source": [
-    "# create minimum eigen optimizer based on VQE\n",
-    "import warnings\n",
-    "\n",
-    "warnings.filterwarnings(\"ignore\", category=UserWarning)\n",
+    "# create minimum eigen optimizer based on SamplingVQE\n",
     "vqe_optimizer = MinimumEigenOptimizer(vqe)\n",
     "\n",
     "# solve quadratic program\n",
@@ -983,7 +977,7 @@
     {
      "data": {
       "text/html": [
-       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td><code>qiskit-terra</code></td><td>0.22.0.dev0+4749eb5</td></tr><tr><td><code>qiskit-aer</code></td><td>0.11.0</td></tr><tr><td><code>qiskit-nature</code></td><td>0.5.0</td></tr><tr><td><code>qiskit-finance</code></td><td>0.3.4</td></tr><tr><td><code>qiskit-optimization</code></td><td>0.5.0</td></tr><tr><td><code>qiskit-machine-learning</code></td><td>0.5.0</td></tr><tr><th>System information</th></tr><tr><td>Python version</td><td>3.8.13</td></tr><tr><td>Python compiler</td><td>Clang 12.0.0 </td></tr><tr><td>Python build</td><td>default, Mar 28 2022 06:16:26</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>2</td></tr><tr><td>Memory (Gb)</td><td>12.0</td></tr><tr><td colspan='2'>Thu Sep 15 11:56:19 2022 EDT</td></tr></table>"
+       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td><code>qiskit-terra</code></td><td>0.23.0</td></tr><tr><td><code>qiskit-aer</code></td><td>0.11.1</td></tr><tr><td><code>qiskit-optimization</code></td><td>0.5.0</td></tr><tr><td><code>qiskit-machine-learning</code></td><td>0.6.0</td></tr><tr><th>System information</th></tr><tr><td>Python version</td><td>3.9.15</td></tr><tr><td>Python compiler</td><td>Clang 14.0.0 (clang-1400.0.29.102)</td></tr><tr><td>Python build</td><td>main, Oct 11 2022 22:27:25</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Tue Dec 06 21:47:47 2022 JST</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -1036,7 +1030,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.13"
+   "version": "3.9.7"
   }
  },
  "nbformat": 4,
diff --git a/docs/tutorials/07_examples_vehicle_routing.ipynb b/docs/tutorials/07_examples_vehicle_routing.ipynb
index c4c562948..ac72c649a 100644
--- a/docs/tutorials/07_examples_vehicle_routing.ipynb
+++ b/docs/tutorials/07_examples_vehicle_routing.ipynb
@@ -80,7 +80,7 @@
     "\n",
     "Here, we demonstrate an approach that combines classical and quantum computing steps, following the quantum approximate optimization approach of Farhi, Goldstone, and Gutmann (2014). In particular, we use the variational quantum eigensolver (VQE). We stress that given the use of limited depth of the quantum circuits employed (variational forms), it is hard to discuss the speed-up of the algorithm, as the solution obtained is heuristic in nature. At the same time, due to the nature and importance of the target problems, it is worth investigating heuristic approaches, which may be worthwhile for some problem classes. \n",
     "\n",
-    "Following [5], the algorithm can be summarized as follows:\n",
+    "The algorithm can be summarized as follows:\n",
     "\n",
     "- Preparation steps: \n",
     "\t- Transform the combinatorial problem into a binary polynomial optimization problem with equality constraints only;\n",
@@ -168,10 +168,8 @@
    "source": [
     "## Initialization\n",
     "\n",
-    "First of all we load all the packages that we need: \n",
-    "  - Python 3.6 or greater is required;\n",
-    "  - CPLEX 12.8 or greater is required for the classical computations;\n",
-    "  - Latest Qiskit is required for the quantum computations."
+    "First of all we load all the packages that we need.\n",
+    "CPLEX is required for the classical computations. You can install it by `pip install 'qiskit-optimization[cplex]'`. "
    ]
   },
   {
@@ -180,16 +178,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Load the packages that are required\n",
     "import numpy as np\n",
-    "import operator\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
-    "import sys\n",
-    "\n",
-    "if sys.version_info < (3, 6):\n",
-    "    raise Exception(\"Please use Python version 3.6 or greater.\")\n",
-    "\n",
     "try:\n",
     "    import cplex\n",
     "    from cplex.exceptions import CplexError\n",
@@ -197,13 +188,11 @@
     "    print(\"Warning: Cplex not found.\")\n",
     "import math\n",
     "\n",
-    "# Qiskit packages\n",
-    "from qiskit import BasicAer\n",
-    "from qiskit.quantum_info import Pauli\n",
-    "from qiskit.utils import QuantumInstance, algorithm_globals\n",
-    "from qiskit.algorithms import NumPyMinimumEigensolver, VQE\n",
-    "from qiskit.circuit.library import TwoLocal\n",
-    "from qiskit.algorithms.optimizers import SPSA"
+    "from qiskit.utils import algorithm_globals\n",
+    "from qiskit.algorithms.minimum_eigensolvers import SamplingVQE\n",
+    "from qiskit.algorithms.optimizers import SPSA\n",
+    "from qiskit.circuit.library import RealAmplitudes\n",
+    "from qiskit.primitives import Sampler"
    ]
   },
   {
@@ -436,14 +425,12 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -499,7 +486,7 @@
     "\n",
     "- `binary_representation` : encodes the problem $(M)$ into a QP terms (that's basically linear algebra);\n",
     "- `construct_problem` : constructs a QUBO optimization problem as an instance of `QuadraticProgram`;\n",
-    "- `solve_problem`: solves the problem $(M)$ constructed at the previous step via `MinimunEigenOptimizer` by using VQE with default parameters;"
+    "- `solve_problem`: solves the problem $(M)$ constructed at the previous step via `MinimunEigenOptimizer` by using `SamplingVQE` with default parameters;"
    ]
   },
   {
@@ -593,13 +580,7 @@
     "\n",
     "    def solve_problem(self, qp):\n",
     "        algorithm_globals.random_seed = 10598\n",
-    "        quantum_instance = QuantumInstance(\n",
-    "            BasicAer.get_backend(\"qasm_simulator\"),\n",
-    "            seed_simulator=algorithm_globals.random_seed,\n",
-    "            seed_transpiler=algorithm_globals.random_seed,\n",
-    "        )\n",
-    "\n",
-    "        vqe = VQE(quantum_instance=quantum_instance)\n",
+    "        vqe = SamplingVQE(sampler=Sampler(), optimizer=SPSA(), ansatz=RealAmplitudes())\n",
     "        optimizer = MinimumEigenOptimizer(min_eigen_solver=vqe)\n",
     "        result = optimizer.solve(qp)\n",
     "        # compute cost of the obtained result\n",
@@ -730,30 +711,30 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "nbsphinx-thumbnail"
+    ]
+   },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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332QQwB9/FMsqdYKtiIiIFJ+YGNKHD6c/cGbdOjjvhpqXSmFFREREis/QoaS/9BKfA/Z69aAYTrBVWBERESln7PbSdacdtz5nRURERC7ftrgk5qw/xNr9p9gdf4bMbBMvD4O6YRVoFV2Zvi0iiakebHWZBVJYERERKaP2J6Qyau4W1u47hYfNIPu8PSqZ2SY7jqbw5/EzzFh9gFa1KvPabU2IDgmwsGLndBhIRESkDJq3OY6ub6xgw4HTAHmCyvlyl284cJqub6xg3ua4EquxsLRnRUREpIxZsPUoI77cSlHOTMm2m2RjMuKLzQD0alrdJbVdCu1ZERERKWOe+2ZbkYLK+UzgqTlb2J+QWpwlXRaFFRERkTIm+5Kjyt/PN01Gzd1STNVcPpeGlSlTptCkSRPHFWjbtGnDjz/+6MpNioiIlFt/HEkGCj4/pbCy7SZr951iW1xScZR12VwaVmrUqMGrr77Khg0bWL9+PTfccAO9evVi+/btrtysiIhIufRtMZ4c62EzmLP+ULGt73K4NKz07NmTm266iSuuuIJ69erx8ssvU6FCBdasWePKzYqIiJRLuTN/ikO23WTd/uJb3+UosdlA2dnZzJkzh9TUVNq0aeN0THp6Ounp6Y7vk5NzdmdlZmaSmZlZInVeqtz63L3Okqa+OKe+5KeeOKe+OKe+OBd3MgWiwMfm/DCQabeTmXAI77CoQq3vYEJy0Xu8aRN+Dz/MasC/UyfYvz9n+fvvw/z5/z/um2+gWrVCrdIwTdOl19zdunUrbdq04dy5c1SoUIFZs2Zx0003OR07duxYxo0bl2/5rFmz8Pf3d2WZIiIiUgyqbN3Ktc8/f/GB+/ZBdHSh1unysJKRkcHBgwdJSkriq6++4sMPP2T58uU0atQo31hne1YiIyNJSEggKCjIlWVetszMTGJjY+nSpYtuV34e9cU59SU/9cQ59cU59cW5a15ayAtXZ/P8ehvpdgPs2aTu/JWUdd+SeeqwY1zFjvcS2Kz7RdfnZTPY9ELXIteRnJxMSEgISUlJxfL/t8sPA3l7e1O3bl0Amjdvzrp163jzzTd5//3384318fHBx8cn33IvL69S82YsTbWWJPXFOfUlP/XEOfXFOfUlr+pVAoFE0jOySNi8mOTf5pKVdByv8Dpknj0LgGdITVKO7MG7iXHR9dUJC7qk/hb3v0mJX2fFbrfn2XsiIiIixaNJWM4f/EemPcKpRVPwrlaPkFueJvP4HgCqPzQNn/A6ZJzYd9F1edgMWkZXcmm9heXSPSvPPPMMN954IzVr1iQlJYVZs2axbNkyFi5c6MrNioiIlCunT5/m7bffZuq7U2j5/nv4Rl1FUO8x2PwCOfxWfwDC+7+KZ1AoXqHRpP25GtO0YxgF77PItpv0bRFZUi/hglwaVuLj47nnnns4evQowcHBNGnShIULF9KlSxdXblZERKRcOH78OJMmTeKdyZNJz8iicvOcCSyVuz7MuUw7B1/vlfN9lwfxjYwBwDs0GjPzHFlJ8XhVrOp0vR42g+ZRlYipHlwyL+QiXBpWPvroI1euXkREpFw6cOAAr732Gh9+9DF2w4b/VTdRuWUv/IMqAtlgmo6g4t+wA4FX3+x4rldYLQAy4/cVHFYMg9dua+Lql1FouuuyiIhIKbFr1y7Gj3+VTz/7FJu3P/4tbyOweU88fCv8PSJngm/83P8AYPMLIvSWp/KswyOgEja/IDJO7Me/Xv7rnhnA632bEB0S4MqXUiQKKyIiIm5u8+bNvPTyy3w9dy5egZUJaj+QCk27Y/P2czo+/VDObW1qDP8032OGYeAdFk1mfN6TbD1sBh6Gwet9m9CrafXifxGXQWFFRETETa1atYoXX3qJn378EZ9K1ajU9WEqxHTG8HQ+Nfjs3vXQqhkAkY/PKfAEWq+Q6Jyx5ISUbLtJi6hKTLjNvfao5FJYERERcSOmabJ48WL+8+JLrPxlBb6hUVS5eSQBDa/DsHkU+LzMk4c4Pe81uPNzqt37DtkF7HUB8A6LJmXD99Sr7EWb+hH0bRHpNifTOqOwIiIi4gbsdjvfffcdL770Mhs3rMcvoh6ht/4bvytaX3CKMYA9PZUjHz6En19OQPEMDiM7u+DxXqG1AJOXOlakVauYYnwVrlHiF4UTERGR/5eVlcVnn31Go5grufXWW9mZkE7Y7S8SOuC/+Ndrc9GgYtqzOfS/OwAIbn9XobbpFRKJYdjYsmXLZddfErRnRURExALp6enMmDGDl8e/ysH9+/Cv05Lwu17Dt0b+e+ddyMFJfQDwq9OSoBa9gAvsUvmbzcsX35DqbN269VJKL3EKKyIiIiVs06ZN3HhTD44fO4rh40/FjoPwi74am38wZnYWhkfh/nuOn/siZGeCzYOwPmPInbpcGEblKDZt/v0SX0HJUlgREREpYb6+vvj4+uEfUIG01DMkLptOItMdj3v5B+IZUBHDLxh8g7AFVMTDPxgP/4qOr9N2reLs7t8AqDny6yLX4BUWzZYtP2CaJoZx8ZsaWklhRUREpIQ1bNiQA/tybi549uxZ4uPjC/w4dvw4R44e4sRfGzl1KoHsrKy8K7PZOD51CB7+wfgGh0Kr0ZxePpNMzwA8/g42Nv+KeAQEY/MLxuaVc7ND79BanEg8zZEjR6he3b2uq/JPCisiIiIW8vPzIyoqiqioqIuOtdvtzJ8/n169ci6lP27cOEJCQhzB5vTp0wCEJO4g7vBhEpMS863D09cfr4CKmB7eAGzdulVhRURERIpHQkKCI6g8++yzvPDCC3kez8zMZMGCBfy+aSNeXl5kZGSQkJDA8ePHne61sdtN2rdvb8VLKRKFFRERkVIgKyuL8PBwANq3b8/LL7980ed4e3sTERFBRESEq8tzKV1nRUREpBTw8ck51yQoKIgVK1ZYXE3JUlgRERFxc7Vq1cJutwM4zkspTxRWRERE3Fi/fv3Yv38/AKmpqdhs5e+/7vL3ikVEREqJd999ly+++AKAXbt24e/vb3FF1lBYERERcUPr1q1j2LBhAMyePZt69epZXJF1FFZERETcTGJiIq1atQJg+PDh3H777RZXZC2FFRERETdit9upVKkSAFdffTVvvfWWxRVZT2FFRETEjfj6+jo+b9iwweJq3IPCioiIiJto3LgxmZmZQM7MH8mhsCIiIuIG7r//fv744w8AkpKSyuUU5YKoEyIiIhabOXMmH3zwAQC///47QUFBFlfkXhRWRERELLRt2zYGDhwIwEcffUSTJk0srsj9KKyIiIhY5MyZM1x55ZUADBo0iHvvvdfiityTwoqIiIgF7HY7gYGBANSvX59p06ZZXJH7UlgRERGxQG5Q8fDwYOfOnRZX494UVkREREpYq1atSEtLAyAjI8PiatyfwoqIiEgJeuKJJ1i3bh0AJ0+e1BTlQlCHRERESsjcuXN54403AFizZg2VK1e2uKLSQWFFRESkBOzZs4c+ffoA8NZbb9G6dWuLKyo9FFZERERc7Ny5c9StWxeAPn36MHz4cIsrKl0UVkRERFzIbrfj5+cHQGRkJHPmzLG4otLHpWFl/PjxtGzZksDAQMLCwujduze7du1y5SZFRETcSkhICACGYXDw4EGLqymdXBpWli9fzrBhw1izZg2xsbFkZmbStWtX3UlSRETKhRtuuIHTp08DmqJ8OTxdufKffvopz/fTp08nLCyMDRs2cN111+Ubn56eTnp6uuP75ORkADIzMx23zHZXufW5e50lTX1xTn3JTz1xTn1xrjT05ZVXXmHNmjX4+fnx559/Ypqmy+t1l74U9/YN0zTNYl3jBezevZsrrriCrVu3EhMTk+/xsWPHMm7cuHzLZ82ahb+/f0mUKCIiIpcpLS2N/v37k5SUVCx3kC6xsGK327nllltITExk5cqVTsc427MSGRlJQkKC298uOzMzk9jYWLp06YKXl5fV5bgN9cU59SU/9cQ59cU5d+7LkSNHaNiwIQDPP/88Tz75ZIlt2136kpycTEhISLGFFZceBjrfsGHD2LZtW4FBBcDHxwcfH598y728vNzuzViQ0lRrSVJfnFNf8lNPnFNfnHO3vmRkZBAdHQ1At27deOaZZyypw+q+FPe2SySsPPLII8yfP58VK1ZQo0aNktikiIhIicv9gzs0NDTfeZty6VwaVkzTZPjw4XzzzTcsW7aMWrVquXJzIiIilomIiHB8HR8fb2ElZY9Lw8qwYcOYNWsW8+bNIzAwkGPHjgEQHBzsuECOiIhIadezZ0+OHj0KkOfcSykeLr3OypQpU0hKSqJjx45Uq1bN8TF79mxXblZERKTEvP7668yfPx+A/fv34+3tbXFFZY/LDwOJiIiUVb/88gujRo0C4PvvvycqKsriisom3RtIRETkEsTHxzsucDp69GhuvvlmiysquxRWREREiigrK4vw8HAA2rVrx/jx4y2uqGxTWBERESmi3CnKQUFBF7x+mBQPhRUREZEiqFOnDna7HcBxk0JxLYUVERGRQurfvz979+4FIDU1FZtN/42WBHVZRESkEKZMmcLnn38OwM6dO3WD3RKksCIiInIR69ev5+GHHwbgiy++oH79+hZXVL4orIiIiFxAYmIiLVu2BHLudXfHHXdYXFH5o7AiIiJSALvdTqVKlQBo1qwZb7/9tsUVlU8KKyIiIgXw9fV1fN64caPF1ZRfCisiIiJOXHnllWRmZgI5M3/EOgorIiIi//DAAw+wbds2AJKSkjRF2WLqvoiIyHk+/fRTpk6dCsCmTZsICgqyuCJRWBEREfnb9u3bufvuuwH44IMPaNq0qbUFCaCwIiIiAsCZM2eIiYkBYODAgQwdOtTiiiSXwoqIiJR7drudwMBAAOrVq8f06dOtLUjyUFgREZFyL/e8FA8PD3bt2mVxNfJPCisiIlKuXXPNNY6pyenp6RZXI84orIiISLk1cuRIfvvtNwBOnDiBh4eHxRWJMworIiJSLn3zzTdMmjQJgFWrVhESEmJxRVIQhRURESl39u7dy7/+9S8A3njjDdq0aWNxRXIhCisiIlKunDt3jjp16gDwr3/9ixEjRlhbkFyUwoqIiJQbdrsdPz8/AGrUqMHcuXMtrkgKQ2FFRETKjdDQUMfXhw4dsrASKQqFFRERKRc6derEqVOnABx3U5bSQWFFRETKvLFjx7J06VIA4uLi8PT0tLgiKQqFFRERKdMWLVrEuHHjAFi8eDEREREWVyRFpbAiIiJlVlxcHN26dQPgxRdfpFOnThZXJJdCYUVERMqkjIwMatSoAUDXrl157rnnLK5ILpXCioiIlEk+Pj4AhISEsHDhQourkcuhsCIiImVO9erVHV+fOHHCwkqkOCisiIhImdK7d2+OHDkCwNmzZy2uRoqDwoqIiJQZ//3vf5k3bx4A+/btw9fX1+KKpDi4NKysWLGCnj17EhERgWEYfPvtt67cnIiIlGMrV67kySefBGDevHlER0dbW5AUG5eGldTUVK666iomT57sys2IiEg5l5CQQPv27QEYNWoUt9xyi8UVSXFy6SX8brzxRm688UZXbkJERMq5rKwsxz1/2rZty4QJEyyuSIqbW11vOD09nfT0dMf3ycnJQM49HNz9Pg659bl7nSVNfXFOfclPPXFOfXHu/L6EhYXh5+dHhQoVWLZsWbnulbu8X4p7+4ZpmmaxrrGgDRkG33zzDb179y5wzNixYx2XRD7frFmz8Pf3d2F1IiIiUlzS0tLo378/SUlJBAUFXfb63CqsONuzEhkZSUJCQrG8WFfKzMwkNjaWLl264OXlZXU5bkN9cU59yU89cU59cS63L/feey9nz57l2LFj+Pn5WV2W5dzl/ZKcnExISEixhRW3Ogzk4+PjuOLg+by8vErND2lpqrUkqS/OqS/5qSfOqS95TZs2jfDwcM6ePcvGjRvd/g/akmb1+6W4t63rrIiISKmyceNGRowYAcBHH31EgwYNrC1IXM6le1bOnDnD7t27Hd/v27ePzZs3U7lyZWrWrOnKTYuISBmUmJhI8+bNHYd8+vTpY3FFUhJcGlbWr1/P9ddf7/j+iSeeAGDgwIFMnz7dlZsWEZEyxm63U6lSJQCaNGlicTVSklwaVjp27EgJnb8rIiJlXO7eFB8fH3755RcWLFhgcUVSUnTOioiIuL0mTZqQkZEB5EyLlfJFYUVERNzaQw89xNatWwFISkrCZtN/XeWN/sVFRMRtffbZZ7z33nsAbNq0SVOUyymFFRERcUs7duxgwIABALz//vs0bdrU2oLEMgorIiLidlJTU2nUqBEA99xzD/fff7/FFYmVFFZERMSt2O12KlSoAMAVV1zBjBkzLK5IrKawIiIibiU4OBgADw8P/vzzT4urEXegsCIiIm6jbdu2nDlzBoBz585ZXI24C4UVERFxC0899RSrV68G4MSJE3h6utW9dsVCCisiImK5efPmMXHiRABWrlxJSEiIxRWJO1FYERERS+3bt4/evXsDMGnSJNq1a2dtQeJ2FFZERMQy586do3bt2gD07t2bxx9/3OKKxB0prIiIiGVyb05YvXp1vvnmG4urEXelsCIiIpY4/7yUw4cPW1iJuDuFFRERKXFdunTh5MmTAKSnp1tcjbg7hRURESlR//nPf1i8eDEAcXFxeHt7W1yRuDuFFRERKTGxsbGMGTPG8XVERITFFUlpoLAiIiIl4siRI3Tt2hWAcePG0blzZ4srktJCYUVERFwuIyOD6tWrA9C5c2deeOEFiyuS0kRhRUREXM7HxweAKlWqEBsba3E1UtoorIiIiEvVqFHD8XVCQoKFlUhppbAiIiIuc+uttxIXFwfA2bNnLa5GSiuFFRERcYlJkybx7bffArB37158fX2tLUhKLYUVEREpdr/++isjR44E4Ntvv6VWrVoWVySlmcKKiIgUq4SEBK699loAnnrqKXr16mVxRVLaKayIiEixycrKIjQ0FIA2bdrw2muvWVyRlAUKKyIiUmxyz0upUKECq1atsrgaKSsUVkREpFjUq1eP7OxsAJKSkiyuRsoShRUREbls99xzD3/99RcAqamp2Gz670WKj95NIiJyWaZOnconn3wCwB9//IG/v7/FFUlZo7AiIiKXbOPGjTzwwAMAfPrppzRs2NDiiqQsUlgREZFLkpycTPPmzQF48MEHueuuuyyuSMoqhRURESkyu91OcHAwAE2aNGHKlCkWVyRlWYmElcmTJxMdHY2vry+tW7dm7dq1JbFZERFxkdzzUry9vfn9998trkbKOpeHldmzZ/PEE08wZswYNm7cyFVXXUW3bt2Ij4939aZFRMQFmjZtSnp6OqCbE0rJcHlYmTRpEvfddx+DBw+mUaNGvPfee/j7+/Pxxx+7etMiIlLMhg0b5tiTkpiYqCnKUiI8XbnyjIwMNmzYwDPPPONYZrPZ6Ny5M6tXr843Pj093ZHWIefkLYDMzEwyMzNdWeply63P3essaeqLc+pLfuqJc+7Ul6+++opp06bh5+fHihUr8Pf3t6wud+qLO3GXvhT39g3TNM1iXeN5jhw5QvXq1Vm1ahVt2rRxLB81ahTLly/nt99+yzN+7NixjBs3Lt96Zs2apXn7IiIipURaWhr9+/cnKSmJoKCgy16fS/esFNUzzzzDE0884fg+OTmZyMhIunbtWiwv1pUyMzOJjY2lS5cueHl5WV2O21BfnFNf8lNPnHOHvpw9e5aqVasCcMcddzB16lRL6jifO/TFHblLX3KPjBQXl4aVkJAQPDw8OH78eJ7lx48fd7zxz+fj44OPj0++5V5eXqXmzViaai1J6otz6kt+6olzVvXl/CnKderUYdq0aSVew4Xo/eKc1X0p7m279Mwob29vmjdvzpIlSxzL7HY7S5YsyXNYSERE3FNuULHZbOzevdviaqS8cvlhoCeeeIKBAwfSokULWrVqxf/+9z9SU1MZPHiwqzctIiKXoV27dpw5cwYgz+QHkZLm8rByxx13cOLECV544QWOHTtG06ZN+emnnwgPD3f1pkVE5BI9/fTTrFq1CoATJ07g6elWpzhKOVMi775HHnmERx55pCQ2JSIil+m7777jtddeA+CXX34hJCTE4oqkvNPVfERExGH//v306tULgIkTJ3LttddaXJGIwoqIiPzt3Llz1KpVC4BevXoxcuRIiysSyaGwIiIiAPj5+QEQERHBt99+a20xIudRWBEREUJDQx1fx8XFWViJSH4KKyIi5VzXrl1JSEgANEVZ3JPCiohIOfbSSy8RGxsLwKFDh/D29ra4IpH8FFZERMqpJUuW8PzzzwPw008/UaNGDYsrEnFOYUVEpBw6cuQInTt3BmDMmDF069bN4opECqawIiJSzmRlZVG9enUAOnXqxNixY60tSOQiFFZERMqZ3DviVq5cmcWLF1tcjcjFKayIiJQjkZGRjq9PnDhhYSUihaewIiJSTtx2220cPnwYgLNnz2Kz6b8AKR30ThURKQfefPNNvv76awD27NmDr6+vxRWJFJ7CiohIGbd69WpGjBgBwNdff03t2rWtLUikiBRWRETKsISEBNq2bQvAyJEjufXWWy2uSKToFFZERMqorKwsxz1/WrduzcSJEy2uSOTSKKyIiJRRueelBAQEsGbNGourEbl0CisiImVQvXr1yM7OBiA5OdniakQuj8KKiEgZM3DgQP766y8AUlJSNEVZSj29g0VEypCPPvqImTNnArBt2zYqVKhgcUUil09hRUSkjNi8eTNDhw4F4JNPPqFx48YWVyRSPBRWRETKgOTkZJo1awbA/fffz4ABAyyuSKT4KKyIiJRydrud4OBgAK688kref/99iysSKV4KKyIipVxAQAAAXt7ebNmyxeJqRIqfwoqISCnWrFkzzp07B8C5s2ctrkbENRRWRERKqeHDh7N582YATp8+rSnKUmbpnS0iUgrNnj2bd955B4B169ZRsWJFawsScSGFFRGRUmbXrl3ceeedAEyZMoUWLVpYXJGIaymsiIiUImlpaTRo0ACA/v378+CDD1pckYjrKayIiJQSdrvdMfOndu3afPbZZxZXJFIyFFZEREqJ3PNSbDYbe/bssbYYkRKksCIiUgp0796dlJQUANLT0y2uRqRkKayIiJQCq1evBuD48eN4enpaXI1IyVJYERFxYz/99JPj619++YWwsDALqxGxhsvCyssvv0zbtm3x9/fX/H8RkUtw4MAB7rjjDgBeeuklrr32WosrErGGy8JKRkYGffv25aGHHnLVJkREyqxz584RHR3t+H748OHWFSNiMZcd+Bw3bhwA06dPd9UmRETKLD8/PwCqVq1qcSUi1nOrs7TS09PznOWenJwMQGZmJpmZmVaVVSi59bl7nSVNfXFOfclPPfl/derUcYSVbdu2ERsbq778g94vzrlLX4p7+4ZpmmaxrvEfpk+fzogRI0hMTLzo2LFjxzr2yJxv1qxZ+Pv7u6A6ERERKW5paWn079+fpKQkgoKCLnt9RdqzMnr0aCZMmHDBMTt27HBcCrqonnnmGZ544gnH98nJyURGRtK1a9diebGulJmZSWxsLF26dMHLy8vqctyG+uKc+pKfegITJ07kxRdfBHJ+l0ZERKgvBVBfnHOXvuQeGSkuRQorI0eOZNCgQRccU7t27UsuxsfHBx8fn3zLvby8Ss2bsTTVWpLUF+fUl/zKa0+WLl3Ks88+C8CPP/5IVFRUnsfLa18uRn1xzuq+FPe2ixRWQkNDCQ0NLdYCRETKu2PHjtGpUycAXnjhBbp3725xRSLuxWUn2B48eJBTp05x8OBBsrOz2bx5MwB169alQoUKrtqsiEipkpWVRbVq1QC4/vrrnZ63J1LeuSysvPDCC8yYMcPxfbNmzQD4+eef6dixo6s2KyJSqnh7ewNQqVIlli5danE1Iu7JZReFmz59OqZp5vtQUBERyVGzZk1yJ2QmJCRYXI2I+9K9gURELNCnTx8OHToEwNmzZ7HZ9OtYpCD66RARKWFvv/02c+fOBWD37t34+vpaXJGIe1NYEREpQatXr+bRRx8F4KuvvqJOnToWVyTi/hRWRERKyKlTp2jbti0Ajz/+OLfddpvFFYmUDgorIiIlwG63U6VKFQBatmzJpEmTLK5IpPRQWBERKQHef1+d29/fn7Vr11pcjUjporAiIuJiDRo0IDsrC4CUlBSLqxEpfRRWRERcaPDgwezatQvICSqaoixSdPqpERFxkY8++ojp06cDsHXrVt1qROQSKayIiLjAli1bGDp0KAAzZswgJibG4opESi+FFRGRYpacnMxVV10FwH333cc999xjcUUipZvCiohIMbLb7QQHBwPQuHFjpk6danFFIqWfwoqISDEKCAgAwMvLi23btllcjUjZoLAiIlJMmjdvzrlz5wAcn0Xk8imsiIgUg0cffZSNGzcCcPr0aU1RFilG+mkSEblMs2fP5u233wZg3bp1VKxY0dqCRMoYhRURkcvw119/ceeddwIwefJkWrRoYXFFImWPwoqIyCVKS0ujXr16ANx55508/PDDFlckUjYprIiIXAK73e6Y+VOrVi0+//xziysSKbsUVkRELkGlSpUAMAyDvXv3WlyNSNmmsCIiUkTXXXcdycnJAGRkZFhcjUjZp7AiIlIEzz77LL/88gsAx48fx9PT0+KKRMo+hRURkUKaP38+48ePB2D58uWEhYVZXJFI+aCwIiJSCAcOHKBnz54ATJgwgeuuu87iikTKD4UVEZGLyMjIIDo6GoAePXowatQoawsSKWcUVkRELsLHxweAqlWrMn/+fIurESl/FFZERC4gPDzc8fXRo0ctrESk/FJYEREpwI033kh8fDwA6enpFlcjUn4prIiIODF+/Hh++uknAA4ePIi3t7fFFYmUXworIiL/sGzZMp599lkAFixYQGRkpMUViZRvCisiIuc5duwY119/PQDPPfccN954o8UViYjCiojI37KysqhWrRoAHTp04MUXX7S4IhEBhRUREYfc81IqVqzIsmXLrC1GRBxcFlb279/PkCFDqFWrFn5+ftSpU4cxY8bopl8i4paio6MxTROAkydPWlyNiJzPZXfg2rlzJ3a7nffff5+6deuybds27rvvPlJTU5k4caKrNisiUmS33347Bw4cAODs2bPYbNrpLOJOXBZWunfvTvfu3R3f165dm127djFlyhSFFRFxG++88w5z5swB4K+//sLX19fiikTkn0r03uZJSUlUrly5wMfT09PzXHgpOTkZgMzMTDIzM11e3+XIrc/d6yxp6otz6kt+VvRk/fr1jBo1Cj8/P2bOnElUVJTb/ZvoveKc+uKcu/SluLdvmLkHaV1s9+7dNG/enIkTJ3Lfffc5HTN27FjGjRuXb/msWbPw9/d3dYkiIiJSDNLS0ujfvz9JSUkEBQVd9vqKHFZGjx7NhAkTLjhmx44dNGjQwPF9XFwcHTp0oGPHjnz44YcFPs/ZnpXIyEgSEhKK5cW6UmZmJrGxsXTp0gUvLy+ry3Eb6otz6kt+JdkTu91OpUqVAGjWrJlbz/zRe8U59cU5d+lLcnIyISEhxRZWinwYaOTIkQwaNOiCY2rXru34+siRI1x//fW0bduWqVOnXvB5Pj4+jrubns/Ly6vUvBlLU60lSX1xTn3JryR64uXlRVZWFn5+fvz6668u3VZx0XvFOfXFOav7UtzbLnJYCQ0NJTQ0tFBj4+LiuP7662nevDnTpk3TGfYiYrmGDRuSlZUFwJkzZyyuRkQKw2Un2MbFxdGxY0eioqKYOHEiJ06ccDxWtWpVV21WRKRA9957Lzt37gQgJSVFf0CJlBIuCyuxsbHs3r2b3bt3U6NGjTyPldA5vSIiDtOmTWPatGkAbN26lQoVKlhckYgUlsv+rBg0aBCmaTr9EBEpSVu2bOHee+8FYPr06cTExFhckYgUhfaBikiZdubMGa666ioAhgwZwsCBAy2uSESKSmFFRMosu91OYGAgAI0aNbrgpRNExH0prIhImZV7Xoqnpyfbt2+3uBoRuVQKKyJSJrVo0YKzZ88C5LnYpIiUPgorIlLmPPbYY2zYsAGAU6dOaYqySCmnn2ARKVO+/PJL3nrrLQDWrl3ruKy+iJReCisiUmb89ddf3HHHHQC8/fbbtGzZ0uKKRKQ4KKyISJmQlpZGvXr1ALj99tt55JFHLK5IRIqLwoqIlHp2u52AgAAAoqKimD17tsUViUhxUlgRkVKvcuXKABiGwf79+60tRkSKncKKK2RmwpIl8NRT0LIlVKwIXl5QtSrccgv88IPVFYqUGR06dCApKQmAjIwMi6sREVdw2Y0My7Xly6FLl5yvq1aFa6+FgAD44w/4/vucj/vvh/feA8OwtlaRUuzf//43K1asAODo0aN4eupXmkhZpD0rrmCzwW23wYoVcPQozJ8Ps2fD1q3wxRfg4QFTp8Inn1hdqUiptWDBAl555RUAfv75Z6pWrWpxRSLiKgorrnDDDfDVV9C+ff7H7rgDBg3K+XrmzBItS6SsOHjwID169ADg1VdfpWPHjtYWJCIuVa7Dit1uWrPhZs1yPh86ZM32RUqxjIwMoqKiALjpppt4+umnLa5IRFytXB3g3RaXxJz1h1i7/xS748+QmW3i5WFQN6wCraIr07dFJDHVg11fyF9/5XyuVs312xIpY3x8fAAIDw/nB52sLlIulIuwsj8hlVFzt7B23yk8bAbZ5+1Rycw22XE0hT+Pn2HG6gO0qlWZ125rQnRIgGuKOXYMpk/P+fq221yzDZEy6vzzUo4dO2ZhJSJSksr8YaB5m+Po+sYKNhw4DZAnqJwvd/mGA6fp+sYK5m2OK/5isrJgwABISoIrr4QHHij+bYiUUT169OD48eOA7qIsUt6U6T0r8zbHMeKLzRTlzJRsu0k2JiO+2AxAr6bVi6+gBx/Muf5KlSo5J+B6exffukXKsFdffZUFCxYAcODAAbz1syNSrpTZPSv7ElJ5as6WIgWV85nAU3O2sD8htXgKeuwx+OgjqFQJYmPh73uYiMiFrVixgmeeeQaAH374gZo1a1pckYiUtDIbVp6eu4Vs8/Jm+2SbJqPmbrn8YkaOhLfeyrmS7aJF/z8bSEQu6Pjx43To0AGAZ599lptuusniikTECmUyrGw9nMTafacKPD+lsLLtJmv3nWJbXNKlr2TUKJg0CYKDc4JKixaXVZNIeZGVleU4ofa6667j5ZdftrgiEbFKmQwrX204hKeteC5j72EzmLP+Eq+HMno0vP56TlCJjc25T5CIFErueSnBwcEsX77c4mpExEplMqys3X+KrGK64Fu23WTd/tNFf+Jzz8GECTmHfhRURIqkVq1amH8fxj116pTF1YiI1crkbKDd8WcuOiYr+QT2syl4h9e+6Ni/4lOKVsB330HuLuu6dWHyZOfjQkJg4sSirVukjBs8eDD79+8HIDU1FZutTP5NJSJFUObCit1ukpl98b0qJxe8ybkDm8EwCLiyM/51r8G3Zgw2n/wXg8vMNrHbTWyFPbR0/l+C69fnfDgTFaWwIvIPX3/9NQB//vkn/v7+FlcjIu6gzIUVm83Ay8O4aGDxb9A+J6yYJqlbYkndEgsYeEfUwzeqKX7RV+ET0RDD0wsvD6PwQQVyblSYe7NCESmUjRs3Or7+8ssvueKKKyysRkTcSZkLKwB1wyqw4+iFD90ENu1G9pmTJP0667ylJpnx+8k8dYTk1bMxPH3wqdGImjGt2LAhnKZNm+Lh4eHa4kXKoVOnTnH99dfz+eef8+CDD9K3b1+rSxIRN1ImDwa3iq6MRyH2hAS3vQPfqKbY/CsSckvOnVvNrHTMcyl4BIUR3KYvGDZ2L5xBixYtqFwlhH/9619MmTKFP//803ECoIhcOrvdTpUqVRzfT5gwwcJqRMQdlcmw0rdFZKGusWLYPAjp+SSGzUbKxvnUfGoeYX3HAZCdHE/iL5+SnXqaiIc+Jrz/qxgxN7Jw/S6GDR9O/fr1iagRyaBBg/j0008d9ywRkaLx9fXN81lE5J/KZFiJqR5Mq1qF27viEVCRkFtGkR63g8RfPsGvdnOinp5P6L+eAyAzfi+H3+pP4rLpBLW+jdD+r1Fj+OeE9RlDakQLvlz4C3fffTf1/r58/lNPPcW8efNITEx05UsUKRMaNWpEZmYmAEePHrW4GhFxV2XynBWA125rQtc3VpBdiLsD+UbGULHDPSQum45Pjcb412mJ/xXXEPX0fFJ3/ELCdxNIP7KTQ5P64BvVhLA+Y/Gr0xK/OjnXTslOTYSj2wGYOec73nzzTWw2G02vbk63Lp3p3Lkzbdu21V+OIucZOnQoO3bsACA5OVlTlEWkQGX2t0N0SACv921CYefwBLX6F351W3Fy/n/JSop3LA9o2J6op+dTpcfjAJw7sIWD//0X8V+Nw8zO+YvQI6Ai/vXbAhA66C0iHviQil2HsTPVl/++PYVOnToRXLES19/QiVdffZV169aRnZ1drK9XpDSZMWMGH330EQBbtmwhMDDQ4opExJ2V2bAC0Ktpdf53Z1O8PWwXPSRkGDaq3PQ4Nm9/Tsx71RFEclWI6UTU0/Op3O0RAM7uWcfBibdy4ttXMe15g4dXxaoEXtWN0FuepurDM6k2+G0C2g1g7cFknhv7H1q1akWlylXo1bs3kydPZufOnTpZV8qNbdu2Mejvqf0ff/wxV155pbUFiYjbc2lYueWWW6hZsya+vr5Uq1aNu+++myNHjrhyk/n0alqdRY9fR/OoSgAFhhYPm4GHXyAdHnqF7BP7OP3zNKfjApt2J+rp+VTqdD8AabtWcvD1Xpz66R2n4w3DhndYLYJa9ia0z1iqD/+c8Ltew3ZlDxZv2sOjj42gYcOGVI2ozsCBA5k5cyZxcXHF8MpF3M+ZM2cc4WTw4MEMHjzY4opEpDRw6Tkr119/Pc8++yzVqlUjLi6OJ598kj59+rBq1SpXbjaf6JAAvnygDdvikpiz/hDr9p/mr/gUMrNNvDwMrggLpGV0Jfq2iCSmejDv1DjL8OHD8anRiIAG1zpdZ1CLWwhs3pPktXNJXDad1B0rgIc4vWQqfh3uwzCc50DDwxPfGo3wrdEI6I894xzph7dzbv9m5iz6lZkzZwJQp249unfrQqdOnejYsSOVKlVyUXdESobdbncc7mnQoAEff/yxxRWJSGnh0rDy+OOPO76Oiopi9OjR9O7dm8zMTLy8vFy5aadiqgcTUz3Y8X1Bl9AfNmwYy5YvZ978t/AOr41XpQin6zMMg+DWfQhqdRvp678C4MyWxZz47XsCm99CpU5DCwwtuWzevvjVbo5f7eYAZCYcImnt1+zZ/jOTJ09m8uTJ3Hf/A0x9/71LfdkibqFChQoAeHp6Ok6sFREpjBKbDXTq1Ck+++wz2rZtW2BQSU9PJz093fF9cnIyAJmZmY7pjcWtoPNcp77/PtvbX0f8ov8R0PdFDC/vC64nuO1tgJ3Qtn04seorsv6I5cQfsQS27E3Fdv3AyB+K7KmJZMTvJf34XrLi95GdsI/0pAQAQkOq0LRpU5pf3YyePXu67PW7Wm7dpbV+VylvfenYsSMAfn5+nD592unrLm89KSz1xTn1xTl36Utxb98wXXxm59NPP80777xDWloa11xzDfPnz89ztcrzjR07lnHjxuVbPmvWLN3QTEREpJRIS0ujf//+JCUlERQUdNnrK3JYGT169EUvh71jxw4aNGgAQEJCAqdOneLAgQOMGzeO4OBg5s+fj+FkT4OzPSuRkZEkJCQUy4u9FJ988gmPPPIIlbsPJ6Bh+wLH+dhMXmxh5/n1BmlJp8mI30f6sT2c3bGcrJSEPGM7dOhA8+bNadq0KU2bNqVmzZpO+1EWZGZmEhsbS5cuXSw59Oeuyktf5s2bxz333APAkiVLaNGiRYFjy0tPikp9cU59cc5d+pKcnExISEixhZUiHwYaOXKkY9phQWrXru34OiQkhJCQEOrVq0fDhg2JjIxkzZo1tGnTJt/zfHx88PHxybfcy8vLsqYPHjyYn5ct4/PZbxFWORrvkJqOx0zTJDslgYxju7GdPgAt+nDw/YdIOpEz46li5Spc06I5za++ms8//5wDBw4A8NNPPxEQEMBLL71kyWuygpX/hu6sLPdlz549jhsSvvXWW05/5p0pyz25HOqLc+qLc1b3pbi3XeSwEhoaSmho6CVtzG63A+TZe+LuDMPgvSlTWLd+Awe+e5XAdgNIP76HrOO7yYrfQ8aZRAAiakQCfXjsoaE0a9aMq6++msjISMcek/Hjx5OWlkb9+vU5fPgwc+fOxTAM7r77bscMIJGy4ty5c9StWxeAvn37Mnz4cIsrEpHSzGXXWfntt99455132Lx5MwcOHGDp0qX069ePOnXqFPovLHcREBDAN3O/wkg9yYlvX8F7zzI6NAhn9BOP8u2333Lo0CH27dkNwHPPPUfv3r2dHtrx9/fn0KFDpKSkEBYWBuQcZjIMgwcffLDEX5eIK9jtdvz8/ICcWYBffvmlxRWJSGnnstlA/v7+fP3114wZM4bU1FSqVatG9+7dee6555we6nF3DRs2ZMP6dQQHBxMRkX8qc1HOfK5QoQLHjx8nMTGR2rVrc/r0ad5//33ef/99RowYwRtvvFGcpYuUqNwT6A3DYP/+/dYWIyJlgsv2rFx55ZUsXbqUkydPcu7cOfbt28eUKVOoXr26qzbpcg0bNnQaVC5VxYoVOXXqFCdOnHBcLOt///sfhmHw73//u9i2I1JSrr/+escdxzMyMqwtRkTKjDJ9b6DSIiQkhOTkZOLi4hy7z1955RUMw+Dll1+2uDqRwnn++edZtmwZAEePHsXTs8ze1F1ESpjCihuJiIggLS2NAwcO4O2dcxG65557DsMwmDRpksXViRTsxx9/dMxuW7JkCVWrVrW4IhEpSxRW3FDNmjVJT0/nzz//xMPDA8iZMm4YBlOmTLG4OpG8Dh06xE033QTk7BG84YYbLK5IRMoahRU3dsUVV5CVlcW2bdscM4sefvhhDMNgxowZFlcnknNeSs2aOdce6t69O88884zFFYlIWaSwUgo0btwYu93Ohg0bHMsGDRqEYRjMnj3bwsqkvMud2RcaGsqPP/5ocTUiUlYprJQiV199NaZpsmrVKseyO++8E8Mw+O677yysTMqjatWqOb6Oj4+3sBIRKesUVkqhNm3aYJomP//8s2NZr169MAyD2NhYCyuT8qJnz54cO3YMKF1XpBaR0klhpRTr2LEjpmmyYMECx7KuXbtiGAYrVqywsDIpy1577TXmz58PwP79+x0z10REXEVhpQy48cYbMU2Tr7/+2rGsQ4cOGIbBunXrLKxMypoVK1bw9NNPA/D9998TFRVlcUUiUh4orJQht956K6Zp8umnnzqWtWrVCsMw2Lx5s3WFSZkQHx9Phw4dAHjmmWe4+eabLa5IRMoLhZUy6K677sI0TT788EPHsmbNmuHh4cGOHTssrExKq6ysLMLDwwFo3749r7zyisUViUh5orBShg0ZMgTTNHn77beBnLvhNmrUCC8vL/bu3WtxdVKa5E5RDgoK0vlQIlLiFFbKgUceeQTTNHnttdeAnL+S69Spg4+PD4cPH7a4OnF3tWvXxm63A3D69GmLqxGR8khhpRx56qmnME2TsWPHAjlXH42MjCQgIMAxDVXkfP369WPfvn0ApKamYrPpV4aIlDz95imHxowZg2majBo1CoC0tDSqVatGcHAwp06dsrg6cRdTpkzhiy++AGDXrl34+/tbXJGIlFcKK+XYhAkTME2T4cOHA5CcnEyVKlWoUqUKycnJFlcnVlq/fj0PP/wwAF988QX16tWzuCIRKc8UVoS33noL0zQZOnQoAKdOnSI4OJhq1aqRlpZmcXVS0hITE2nZsiWQc77THXfcYXFFIlLeKayIwwcffIBpmvTv3x+AY8eOERAQQM2aNTl37pzF1UlJsNvtVKpUCciZ7p47k0xExEoKK5LPZ599hmma3HrrrQAcOnQIPz8/rrjiCjIyMiyuTlzJ19fX8Xnjxo0WVyMikkNhRQr09ddfk52dTbdu3QDYvXs3Pj4+xMTEkJWVZXF1UtxiYmLIzMwEcmb+iIi4C4UVuSCbzcZPP/1Edna241Lr27dvx8vLixYtWjiuvyGl2/3338/27dsBSEpK0hRlEXEr+o0khWKz2Vi2bBnZ2dm0atUKgA0bNuDh4UG7du0UWkqxmTNn8sEHHwCwefNmgoKCLK5IRCQvhRUpEpvNxm+//UZmZiZNmjQBYNWqVXh4eNC5c2eFllJm+/btDBw4EIAPP/yQq666yuKKRETyU1iRS+Lp6cnvv/9Oeno69evXB2DJkiV4eHjQs2dPhZZS4MyZM8TExAAwaNAghgwZYnFFIiLOKazIZfH29mbnzp2cO3eO6OhoAObPn4+Hhwe33367tcVJgex2O4GBgQDUr1+fadOmWVyRiEjBFFakWPj4+LBv3z5SU1OJiIgAYM6cOQQHB1tcmTiTG1Q8PDzYuXOnxdWIiFyYwooUK39/f+Li4khJSSE0NNSxPDg42HH5drFW69atHVcm1nVzRKQ0UFgRl6hQoQLx8fEcPHjQsWzKlCkYhsHIkSMtrKx8GzlyJGvXrgXg5MmTmqIsIqWCflOJS+UeBtq9ezcBAQEATJo0CcMweP75560srdyZO3cukyZNAmDNmjVUrlzZ4opERApHYUVKRGhoKGfOnCEuLs5xSfeXXnoJwzAYP368xdWVfXv27KFPnz4AvPnmm7Ru3driikRECk9hRUpUREQEZ8+eZf/+/Xh5eQHw7LPPYhgG//vf/6wtrow6d+4cdevWBaBPnz48+uijFlckIlI0CitiiaioKDIyMti5cyceHh4APP744xiGwdSpUy2uruyw2+34+fkBEBkZyZw5cyyuSESk6BRWxFL169cnKyuLrVu3YhgGAA888ACGYTBz5kyLqyv9QkJCADAMI8/JziIipUmJhJX09HSaNm2KYRhs3ry5JDYppUxMTAx2u51169Y5lg0cOBDDMLQ34BJ16tSJ06dPA5qiLCKlW4mElVGjRjkuFCZyIS1atMA0TVauXOlYdvvtt2MYBvPnz7ewstJlzJgxLF26FICjR4/i6elpcUUiIpfO5WHlxx9/ZNGiRUycONHVm5IypF27dpimyZIlSxzLevbsiWEYeZZJfgsXLuQ///kPkHO/pqpVq1pckYjI5XHpn1vHjx/nvvvu49tvv8Xf3/+i49PT00lPT3d8n5ycDEBmZiaZmZkuq7M45Nbn7nWWtMvtS/v27cnIyGDRokX07dsXyAktAD/99BNt2rQpnkJLmKveL0eOHOHWW2/Fz8+P559/nvbt25ea96R+hpxTX5xTX5xzl74U9/YN0zTNYl3j30zT5KabbqJdu3Y899xz7N+/n1q1arFp0yaaNm3q9Dljx45l3Lhx+ZbPmjWrUGFHRERErJeWlkb//v1JSkoiKCjostdX5LAyevRoJkyYcMExO3bsYNGiRXz55ZcsX74cDw+PQoUVZ3tWIiMjSUhIKJYX60qZmZnExsbSpUsXx/VDxHV9mT17Nvfff7/je8Mw+PXXX2ncuHGxbcOVXNGX3KsFh4SEsGfPnmJZZ0nSz5Bz6otz6otz7tKX5ORkQkJCii2sFPkw0MiRIxk0aNAFx9SuXZulS5eyevVqfHx88jzWokUL7rrrLmbMmJHveT4+PvnGA3h5eZWaN2NpqrUkFXdfBgwYwIABA/jwww+57777AGjWrBk2m40//viD+vXrF9u2XKm4+pJ7sT2g1E9R1s+Qc+qLc+qLc1b3pbi3XeSwEhoamuduugV56623eOmllxzfHzlyhG7dujF79mxd6luKzdChQxk6dChvvvkmI0aMwG6306BBAzw9Pdm9ezdRUVFWl+hyvXr14ujRo0DO1WpFRMoal80GqlmzJjExMY6PevXqAVCnTh1q1Kjhqs1KOfXYY49hmqbjPkNZWVlER0fj6+vL4cOHLa7Odf773//y3XffAbB//36neyZFREo7XcFWypTRo0djmqbjjs7p6elERkZSoUIF4uPjLa6ueK1cuZInn3wSgO+++65c7EUSkfKpxMJKdHQ0pmkWeHKtSHH6z3/+g2majv/MU1NTCQ8Pp2LFipw6dcri6i5ffHw87du3B3ICWu50bhGRskh7VqRMe/311zFNk4cffhiApKQkqlSpQkhIiOM6PpcrLS2N1atXs23btouO/fzzL+jStatjJtObb77J9OnTWbBgAevXr+fgwYMXPe8kKyuL8PBwIOfiebmHvkREyipdg1vKhcmTJzN58mTuvfdepk2bxsmTJwkODqZatWrs3r270NfxSU1NZfPmzWzcuJENGzawZu16/tq1A7vdTmhYOFt+33zBK8a+O2UKq9ZvplLkFfTu3ZsXX32N5JMn8o3zr1CBkJBQwsPDqRYeTnh4GOHh4YSFhfHoo48C4Onpyddff012drbjztUiImWRwoqUKx9//DEff/wx/fr144svvuDo0aMEBAQQFRXFzp078fX1dYw9c+YMmzdvZsOGDY5gsuevXdjtdmyeXviG1cIWVpuKXa7Hq3J1Ts9/nTvu7MfSJYsLDA/Nml7Fhp37qNJnLJBN1Qc+pmJGFtlpSdjTkshOTfz760QS05I4mZrEtu1xGBt2YE9NJD3l/w9h5e5hsdlsVKxchdDQUKqFh1O1ak6oyf3IDTm5HwEBAY47XIuIlAYKK1Iuff7553z22Wf861//Yt68eRw4cAA/Pz+io6Np27Yta9dvZM9fuzBNE5unN77h0dhC61Cxa2d8qtbFK6Qmhkfe6whUuvkpfvni34wZMybPtP3zNWnShHPvTMbMzAByAo3h4YVnYAgEhly0btM0sZ9Nxn7uDNlpydjTcsJNdloiR9KSOHQsEfbvgLNryEpNJCM1iT6myTCgEmAAWw2D369swt3r14GuTyEipYDCipQ7ycnJbNq0iQ0bNhAQEEDtulewd/dfQM703yNnPfGOqEelbl3PCyYX/1HxrXklwe0H8PLLL3PttdfSvXv3fGOaNGmCadrJPHWY4L3ZfPj5J4zvMJg/wmsXqnbDMPDwD8bDPxivytUvOv75xe8zZMP3ZBo2lvkEkHwuhRtMkyZbfocbboBFi8DPr1DbFhGxisKKlGlJSUmO80vWr9/A2vUb2LcnJ5h4ePngHV4Lz7B6VLnxJryr1sWrSo18e0yKIuiaPmTE7aBf/7vY8vtmIiMj8zzeuHFjDMMg88QBIn49QL19m9gSXrfQYaUouv65miEbvifF5kknLy82Z6cz6L77uPq++6g0ZAisXAnPPw+6I7qIuDmFFSlzPv30U77/fj6/rVvPgX0598jx8PbFJ7w2HmENqHLTzXhXrYNXlUgMW/GemGoYNir3eJwTM0bQ9/Y7+GXF8jyXnQ4ICCCqVm1SEw5Sbf9qALr9uZrXOwws1jqyUk7ywKLJAEy0GbR76AG+efJJqlf/e2/Mu+9C+/bwzjs5geXv+wqJiLgjhRUpc96e/C5r1+QEAcPbn8BmNxLYrAeewWElsn0PvyAq9RzFus+fZvTo0fz3v//N8/jVTZuyb9NfBB45AkDdU4epkXScw8Hhl73tzMRjJP/2FRW3LqZ5dhYAj61ZTeXmzfMOvPZaiIyEQ4dgwQLo1++yty0i4iq6zoqUOUsXx/Ljjz/y5JNP0rjBFST/Npe49+4l/uOHOLloCmm7VpF97oxLa/Cp3oDgDvcyadIk5s2b51i+LS6Jkz7h3HB8D+bfM3LsGNywe+1lbS/jxAES5v+Xox88gPeh9Uy6q3/OA5Ur5w8quVq0yPm8adNlbVtExNW0Z0XKnICAALp370737t15/XVISEjg559/ZvHixSxctJgDm34Aw8Cv2hV4RTbBN+oqfGo0wuZVvPfVCWxxCxlx2xlwzz38sORX3tmQwtp9pziXXokx59IwyZmdYxrQ5a81zGxe9KvQph/9i5Q1X5L652qqVa/B+P+9wZAhQ/D/6COYORNq1iz4ybnn0+zbd0mvT0SkpCisSJkXEhJC37596du3LwAHDhxgyZIlLF68hEWLFxP/21c505OrN8S7Zk548a52xWWfz2IYBpVvfIzjM0bQteetVLv7dbB5ERwUzrV2u2O3podpcs2hbQSkp5Hqc/GL05mmSfqhbaT8Noe0vRupVacuz330EQMGDMDb2ztnUEpKzueAgIJXVKFCzudiupKviIirKKxIuRMVFcW9997Lvffei2mabN++nSVLlhC7eDE/L/uGxF8+wdM3AO/IGHxqXoVv1FU505cv4UJqNp8AKt/yNMc+fYqExR9SpetD3HA6jn/ON/KyZ9N+/yZ+qt+uwHWZpsm5ves589sc0g79QeOYK3n+iy/o06ePrmArImWawoqUa4ZhEBMTQ0xMDI899hhZWVmsW7eOJUuWsCh2MWtWTOd0ZgbegZUdh4x8o6/CM6jwJ+v6VK1L5c73c2rhZHxrNKLz3g1kQp7AkmnzoPPutU7DimnPJu3P1aT+Noezx/bQslVrXnj3e3r06FFwgAoMzPmcmlpwYWf+Pm8nKKjQr0VExAoKKyLn8fT0pE2bNrRp04bnnnuOtLQ0Vq5cyZIlS1gYu5gtP72FaZr4VqmOZ254iWqCh18Q4SkJhKQmOl9xeF0SazUn/ce36GSzOd2z0uWvNcQc242Zu9Cezdm9GzizdTFZyfG0bNmSoWOfokWPHhg1alz4hURH53w+dKjgMbmP5Y4VEXFTCisiF+Dv70/Xrl3p2rUrEybAqVOn+Pnnn3PCy6LF7J33Y87JulXr8FnKadqdOXnRddoLWB6Ynsr8GSMKfuK6dTkf110Hy5dfeCPNmuV8Pnky5wTaWrXyj1m/Pufz1VdfrGQREUtp6rJIEVSuXJnbbruNd999lz27/+TgwYNM+/hj/nXDNXxuy+IsBYeRXAX90F30h9EwwNcXhgy5eKE1akDLljlfz5qV//GVK3P2rPj4wE03XXx9IiIWUlgRuQyRkZEMGjSITz/9hLcTTxM3bx5JYWFkF/N2sg0b1KsHGzbAPfcU7knPPpvz+dVXYePG/19+8iQ8/HDO1488oqvXiojbU1gRKSaGYVD3lluotHcv3zTpDFx8L8vF5D7/6ys75QSORo0K/+TeveHRR3NOpL3mGrjxRujTB+rWha1boV07ePHFy6xQRMT1FFZEipndz58nbxzByJseJ8PDiyzj0n7MsgwbGR5ePNHjcZ668THsvpdwd+Q334TZs6FNG1i1KufS+jVq5OxtWbpUd1wWkVJBJ9iKFDObzcDLw2DulZ34vdoVTP36ZaISj+Bhmhd/8t+yDRsHKlXj/lufY09IJF4eBjZb0a/zAsDtt+d8iIiUUtqzIuICdcNyrg67O6QmPQa9ycJ6bYr0/IX1rqHHoDfZE5JzSfwrwgKLvUYRkdJCYUXEBVpFV8bj7z0hZ719OV6hCllG4a4ym2V4cLxCFc55+QLgYTNoGV3JZbWKiLg7hRURF+jbIpJse85hH8O003PHCjzNws0R8jSz6bljBYaZc3pttt2kb4tIl9UqIuLuFFZEXCCmejCtauXsXWket4OQtKR8Y3LPYHE2YygkLYmr43biYTNoVasyMdU1vVhEyi+FFREXee22JngYBj12rsx3CCjLsGH38mJaq15OZwxlGR7cvPMXPAyD125rUpJli4i4HYUVEReJDgng9dti8h0CyjZsHKxUjeWTJvFq56HcPOhNDlasRvZ5NyXMPRT0+m0xRIcEWFG+iIjbUFgRcaFeaQcch4ByD/d8FXMDtw75HymROeeh5M4YmhtzQ55xIWlJ9Dp7sIQrFhFxPworIq705ZcAmB4eZHl680SPx3n25scdM31ynfX25Zmbn2DkTY+T5emN6eGR5/kiIuWZwoqIq9jtOVePBYy6dfH+fRP3vj+GAa1r0iA8yDHMy8OgUbUgBrSuyeCpY/D+fRNGnTo5D37xRc56RETKMV3BVsRVzp6FOnWgRw945x3w9yeGnJlCmZmZLFiwgC0vdMXHxzvv86oH59wH6JFHYNeunPUE6LwVESm/FFZEXCUgAFauBFvBOzALvIR+QABMm5azV+UCzxcRKQ/0W1DElS43aCioiIgorIiIiIh7U1gRERERt+bW56yYZs4FyZOTky2u5OIyMzNJS0sjOTkZLy8vq8txG+qLc+pLfuqJc+qLc+qLc+7Sl9z/t3P/H79cbh1WUlJSAIiM1E3cRERESpuUlBSCgy//3maGWVyxxwXsdjtHjhwhMDAQwyhg1oSbSE5OJjIykkOHDhEUFHTxJ5QT6otz6kt+6olz6otz6otz7tIX0zRJSUkhIiICWzFMFHDrPSs2m40aNWpYXUaRBAUF6QfHCfXFOfUlP/XEOfXFOfXFOXfoS3HsUcmlE2xFRETErSmsiIiIiFtTWCkmPj4+jBkzBh8fH6tLcSvqi3PqS37qiXPqi3Pqi3NltS9ufYKtiIiIiPasiIiIiFtTWBERERG3prAiIiIibk1hRURERNyawoqIiIi4NYWVy3Dq1CnuuusugoKCqFixIkOGDOHMmTMXfE7Hjh0xDCPPx4MPPlhCFbvG5MmTiY6OxtfXl9atW7N27doLjp8zZw4NGjTA19eXK6+8kgULFpRQpSWrKH2ZPn16vveFr69vCVbreitWrKBnz55ERERgGAbffvvtRZ+zbNkyrr76anx8fKhbty7Tp093eZ0lrah9WbZsWb73imEYHDt2rGQKLgHjx4+nZcuWBAYGEhYWRu/evdm1a9dFn1fWf7dcSl/Kyu8WhZXLcNddd7F9+3ZiY2OZP38+K1as4P7777/o8+677z6OHj3q+HjttddKoFrXmD17Nk888QRjxoxh48aNXHXVVXTr1o34+Hin41etWkW/fv0YMmQImzZtonfv3vTu3Ztt27aVcOWuVdS+QM7lsc9/Xxw4cKAEK3a91NRUrrrqKiZPnlyo8fv27aNHjx5cf/31bN68mREjRjB06FAWLlzo4kpLVlH7kmvXrl153i9hYWEuqrDkLV++nGHDhrFmzRpiY2PJzMyka9eupKamFvic8vC75VL6AmXkd4spl+SPP/4wAXPdunWOZT/++KNpGIYZFxdX4PM6dOhgPvbYYyVQYclo1aqVOWzYMMf32dnZZkREhDl+/Hin42+//XazR48eeZa1bt3afOCBB1xaZ0kral+mTZtmBgcHl1B11gPMb7755oJjRo0aZTZu3DjPsjvuuMPs1q2bCyuzVmH68vPPP5uAefr06RKpyR3Ex8ebgLl8+fICx5SX3y3nK0xfysrvFu1ZuUSrV6+mYsWKtGjRwrGsc+fO2Gw2fvvttws+97PPPiMkJISYmBieeeYZ0tLSXF2uS2RkZLBhwwY6d+7sWGaz2ejcuTOrV692+pzVq1fnGQ/QrVu3AseXRpfSF4AzZ84QFRVFZGQkvXr1Yvv27SVRrtsqD++Vy9G0aVOqVatGly5d+PXXX60ux6WSkpIAqFy5coFjyuP7pTB9gbLxu0Vh5RIdO3Ys325XT09PKleufMFjx/379+fTTz/l559/5plnnuGTTz5hwIABri7XJRISEsjOziY8PDzP8vDw8AJ7cOzYsSKNL40upS/169fn448/Zt68eXz66afY7Xbatm3L4cOHS6Jkt1TQeyU5OZmzZ89aVJX1qlWrxnvvvcfcuXOZO3cukZGRdOzYkY0bN1pdmkvY7XZGjBhBu3btiImJKXBcefjdcr7C9qWs/G7xtLoAdzN69GgmTJhwwTE7duy45PWff07LlVdeSbVq1ejUqRN79uyhTp06l7xeKd3atGlDmzZtHN+3bduWhg0b8v777/Piiy9aWJm4m/r161O/fn3H923btmXPnj288cYbfPLJJxZW5hrDhg1j27ZtrFy50upS3Eph+1JWfrcorPzDyJEjGTRo0AXH1K5dm6pVq+Y7WTIrK4tTp05RtWrVQm+vdevWAOzevbvUhZWQkBA8PDw4fvx4nuXHjx8vsAdVq1Yt0vjS6FL68k9eXl40a9aM3bt3u6LEUqGg90pQUBB+fn4WVeWeWrVqVSb/M3/kkUcckxdq1KhxwbHl4XdLrqL05Z9K6+8WHQb6h9DQUBo0aHDBD29vb9q0aUNiYiIbNmxwPHfp0qXY7XZHACmMzZs3Azm7dksbb29vmjdvzpIlSxzL7HY7S5YsyZPkz9emTZs84wFiY2MLHF8aXUpf/ik7O5utW7eWyvdFcSkP75Xisnnz5jL1XjFNk0ceeYRvvvmGpUuXUqtWrYs+pzy8Xy6lL/9Uan+3WH2Gb2nWvXt3s1mzZuZvv/1mrly50rziiivMfv36OR4/fPiwWb9+ffO3334zTdM0d+/ebf7nP/8x169fb+7bt8+cN2+eWbt2bfO6666z6iVcti+++ML08fExp0+fbv7xxx/m/fffb1asWNE8duyYaZqmeffdd5ujR492jP/1119NT09Pc+LEieaOHTvMMWPGmF5eXubWrVutegkuUdS+jBs3zly4cKG5Z88ec8OGDeadd95p+vr6mtu3b7fqJRS7lJQUc9OmTeamTZtMwJw0aZK5adMm88CBA6Zpmubo0aPNu+++2zF+7969pr+/v/nUU0+ZO3bsMCdPnmx6eHiYP/30k1UvwSWK2pc33njD/Pbbb82//vrL3Lp1q/nYY4+ZNpvNXLx4sVUvodg99NBDZnBwsLls2TLz6NGjjo+0tDTHmPL4u+VS+lJWfrcorFyGkydPmv369TMrVKhgBgUFmYMHDzZTUlIcj+/bt88EzJ9//tk0TdM8ePCged1115mVK1c2fXx8zLp165pPPfWUmZSUZNErKB5vv/22WbNmTdPb29ts1aqVuWbNGsdjHTp0MAcOHJhn/JdffmnWq1fP9Pb2Nhs3bmz+8MMPJVxxyShKX0aMGOEYGx4ebt50003mxo0bLajadXKn3P7zI7cPAwcONDt06JDvOU2bNjW9vb3N2rVrm9OmTSvxul2tqH2ZMGGCWadOHdPX19esXLmy2bFjR3Pp0qXWFO8izvoB5Pn3L4+/Wy6lL2Xld4thmqZZYrtxRERERIpI56yIiIiIW1NYEREREbemsCIiIiJuTWFFRERE3JrCioiIiLg1hRURERFxaworIiIi4tYUVkRERMStKayIiIiIW1NYEREREbemsCIiIiJu7f8AvEzQfU2VZEoAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -792,7 +773,7 @@
     {
      "data": {
       "text/html": [
-       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td><code>qiskit-terra</code></td><td>0.21.0.dev0+dbd3961</td></tr><tr><td><code>qiskit-aer</code></td><td>0.10.4</td></tr><tr><td><code>qiskit-ibmq-provider</code></td><td>0.19.1</td></tr><tr><td><code>qiskit-optimization</code></td><td>0.4.0</td></tr><tr><th>System information</th></tr><tr><td>Python version</td><td>3.10.4</td></tr><tr><td>Python compiler</td><td>GCC 11.2.0</td></tr><tr><td>Python build</td><td>main, Apr  2 2022 09:04:19</td></tr><tr><td>OS</td><td>Linux</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>14.577545166015625</td></tr><tr><td colspan='2'>Wed May 18 16:05:15 2022 JST</td></tr></table>"
+       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td><code>qiskit-terra</code></td><td>0.23.0</td></tr><tr><td><code>qiskit-aer</code></td><td>0.11.1</td></tr><tr><td><code>qiskit-optimization</code></td><td>0.5.0</td></tr><tr><td><code>qiskit-machine-learning</code></td><td>0.6.0</td></tr><tr><th>System information</th></tr><tr><td>Python version</td><td>3.9.15</td></tr><tr><td>Python compiler</td><td>Clang 14.0.0 (clang-1400.0.29.102)</td></tr><tr><td>Python build</td><td>main, Oct 11 2022 22:27:25</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Tue Dec 06 21:53:30 2022 JST</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -830,8 +811,22 @@
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python"
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
   }
  },
  "nbformat": 4,
diff --git a/docs/tutorials/08_cvar_optimization.ipynb b/docs/tutorials/08_cvar_optimization.ipynb
index 21ab5089c..09ecad0b1 100644
--- a/docs/tutorials/08_cvar_optimization.ipynb
+++ b/docs/tutorials/08_cvar_optimization.ipynb
@@ -13,10 +13,11 @@
    "source": [
     "## Introduction\n",
     "\n",
-    "This notebook shows how to use the Conditional Value at Risk (CVaR) objective function introduced in [1] within the variational quantum optimization algorithms provided by Qiskit. Particularly, it is shown how to setup the `MinimumEigenOptimizer` using `VQE` accordingly. \n",
+    "This notebook shows how to use the Conditional Value at Risk (CVaR) objective function introduced in [1] within the variational quantum optimization algorithms provided by Qiskit. Particularly, it is shown how to setup the `MinimumEigenOptimizer` using `SamplingVQE` accordingly. \n",
     "For a given set of shots with corresponding objective values of the considered optimization problem, the CVaR with confidence level $\\alpha \\in [0, 1]$ is defined as the average of the $\\alpha$ best shots.\n",
     "Thus, $\\alpha = 1$ corresponds to the standard expected value, while $\\alpha=0$ corresponds to the minimum of the given shots, and $\\alpha \\in (0, 1)$ is a tradeoff between focusing on better shots, but still applying some averaging to smoothen the optimization landscape.\n",
     "\n",
+    "\n",
     "## References\n",
     "\n",
     "[1] [P. Barkoutsos et al., *Improving Variational Quantum Optimization using CVaR,* Quantum 4, 256 (2020).](https://quantum-journal.org/papers/q-2020-04-20-256/)"
@@ -30,15 +31,12 @@
    "source": [
     "from qiskit.circuit.library import RealAmplitudes\n",
     "from qiskit.algorithms.optimizers import COBYLA\n",
-    "from qiskit.algorithms import NumPyMinimumEigensolver, VQE\n",
-    "from qiskit.opflow import PauliExpectation, CVaRExpectation\n",
-    "from qiskit_optimization import QuadraticProgram\n",
+    "from qiskit.algorithms.minimum_eigensolvers import NumPyMinimumEigensolver, SamplingVQE\n",
+    "from qiskit.primitives import Sampler\n",
     "from qiskit_optimization.converters import LinearEqualityToPenalty\n",
     "from qiskit_optimization.algorithms import MinimumEigenOptimizer\n",
     "from qiskit_optimization.translators import from_docplex_mp\n",
-    "from qiskit import execute\n",
     "from qiskit.utils import algorithm_globals\n",
-    "from qiskit_aer import Aer\n",
     "\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
@@ -155,7 +153,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Minimum Eigen Optimizer using VQE"
+    "## Minimum Eigen Optimizer using SamplingVQE"
    ]
   },
   {
@@ -172,10 +170,8 @@
     "ansatz = RealAmplitudes(n, reps=1)\n",
     "m = ansatz.num_parameters\n",
     "\n",
-    "# set backend\n",
-    "backend_name = \"qasm_simulator\"  # use this for QASM simulator\n",
-    "# backend_name = 'aer_simulator_statevector'  # use this for statevector simlator\n",
-    "backend = Aer.get_backend(backend_name)\n",
+    "# set sampler\n",
+    "sampler = Sampler()\n",
     "\n",
     "# run variational optimization for different values of alpha\n",
     "alphas = [1.0, 0.50, 0.25]  # confidence levels to be evaluated"
@@ -191,13 +187,13 @@
      "output_type": "stream",
      "text": [
       "alpha = 1.0:\n",
-      "objective function value: 0.7296000000000049\n",
-      "variable values: x_0=0.0, x_1=1.0, x_2=1.0, x_3=0.0, x_4=1.0, x_5=0.0\n",
+      "objective function value: 1.2783500000000174\n",
+      "variable values: x_0=1.0, x_1=1.0, x_2=0.0, x_3=0.0, x_4=1.0, x_5=0.0\n",
       "status: SUCCESS\n",
       "\n",
       "alpha = 0.5:\n",
-      "objective function value: 0.7296000000000049\n",
-      "variable values: x_0=0.0, x_1=1.0, x_2=1.0, x_3=0.0, x_4=1.0, x_5=0.0\n",
+      "objective function value: 1.2783500000000174\n",
+      "variable values: x_0=1.0, x_1=1.0, x_2=0.0, x_3=0.0, x_4=1.0, x_5=0.0\n",
       "status: SUCCESS\n",
       "\n",
       "alpha = 0.25:\n",
@@ -217,26 +213,22 @@
     "def callback(i, params, obj, stddev, alpha):\n",
     "    # we translate the objective from the internal Ising representation\n",
     "    # to the original optimization problem\n",
-    "    objectives[alpha] += [-(obj + offset)]\n",
+    "    objectives[alpha].append(np.real_if_close(-(obj + offset)))\n",
     "\n",
     "\n",
     "# loop over all given alpha values\n",
     "for alpha in alphas:\n",
     "\n",
-    "    # initialize CVaR_alpha objective\n",
-    "    cvar_exp = CVaRExpectation(alpha, PauliExpectation())\n",
-    "    cvar_exp.compute_variance = lambda x: [0]  # to be fixed in PR #1373\n",
-    "\n",
-    "    # initialize VQE using CVaR\n",
-    "    vqe = VQE(\n",
-    "        expectation=cvar_exp,\n",
-    "        optimizer=optimizer,\n",
+    "    # initialize SamplingVQE using CVaR\n",
+    "    vqe = SamplingVQE(\n",
+    "        sampler=sampler,\n",
     "        ansatz=ansatz,\n",
-    "        quantum_instance=backend,\n",
+    "        optimizer=optimizer,\n",
+    "        aggregation=alpha,\n",
     "        callback=lambda i, params, obj, stddev: callback(i, params, obj, stddev, alpha),\n",
     "    )\n",
     "\n",
-    "    # initialize optimization algorithm based on CVaR-VQE\n",
+    "    # initialize optimization algorithm based on CVaR-SamplingVQE\n",
     "    opt_alg = MinimumEigenOptimizer(vqe)\n",
     "\n",
     "    # solve problem\n",
@@ -251,18 +243,20 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "nbsphinx-thumbnail"
+    ]
+   },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 720x360 with 1 Axes>"
+       "<Figure size 1000x500 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -290,9 +284,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "optimal probabilitiy (alpha = 1.00):  0.0000\n",
-      "optimal probabilitiy (alpha = 0.50):  0.0000\n",
-      "optimal probabilitiy (alpha = 0.25):  0.1161\n"
+      "optimal probability (alpha = 1.00):  0.0000\n",
+      "optimal probability (alpha = 0.50):  0.0000\n",
+      "optimal probability (alpha = 0.25):  0.2895\n"
      ]
     }
    ],
@@ -306,17 +300,12 @@
     "ind = np.argsort(objective_values)\n",
     "\n",
     "# evaluate final optimal probability for each alpha\n",
-    "probabilities = np.zeros(len(objective_values))\n",
     "for alpha in alphas:\n",
-    "    if backend_name == \"qasm_simulator\":\n",
-    "        counts = results[alpha].min_eigen_solver_result.eigenstate\n",
-    "        shots = sum(counts.values())\n",
-    "        for key, val in counts.items():\n",
-    "            i = int(key, 2)\n",
-    "            probabilities[i] = val / shots\n",
-    "    else:\n",
-    "        probabilities = np.abs(results[alpha].min_eigen_solver_result.eigenstate) ** 2\n",
-    "    print(\"optimal probabilitiy (alpha = %.2f):  %.4f\" % (alpha, probabilities[ind][-1:]))"
+    "    probabilities = np.fromiter(\n",
+    "        results[alpha].min_eigen_solver_result.eigenstate.binary_probabilities().values(),\n",
+    "        dtype=float,\n",
+    "    )\n",
+    "    print(\"optimal probability (alpha = %.2f):  %.4f\" % (alpha, probabilities[ind][-1:]))"
    ]
   },
   {
@@ -327,7 +316,7 @@
     {
      "data": {
       "text/html": [
-       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td><code>qiskit-terra</code></td><td>0.22.0.dev0+4749eb5</td></tr><tr><td><code>qiskit-aer</code></td><td>0.11.0</td></tr><tr><td><code>qiskit-nature</code></td><td>0.5.0</td></tr><tr><td><code>qiskit-finance</code></td><td>0.3.4</td></tr><tr><td><code>qiskit-optimization</code></td><td>0.5.0</td></tr><tr><td><code>qiskit-machine-learning</code></td><td>0.5.0</td></tr><tr><th>System information</th></tr><tr><td>Python version</td><td>3.8.13</td></tr><tr><td>Python compiler</td><td>Clang 12.0.0 </td></tr><tr><td>Python build</td><td>default, Mar 28 2022 06:16:26</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>2</td></tr><tr><td>Memory (Gb)</td><td>12.0</td></tr><tr><td colspan='2'>Thu Sep 15 11:57:53 2022 EDT</td></tr></table>"
+       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td><code>qiskit-terra</code></td><td>0.23.0</td></tr><tr><td><code>qiskit-aer</code></td><td>0.11.1</td></tr><tr><td><code>qiskit-optimization</code></td><td>0.5.0</td></tr><tr><td><code>qiskit-machine-learning</code></td><td>0.6.0</td></tr><tr><th>System information</th></tr><tr><td>Python version</td><td>3.9.15</td></tr><tr><td>Python compiler</td><td>Clang 14.0.0 (clang-1400.0.29.102)</td></tr><tr><td>Python build</td><td>main, Oct 11 2022 22:27:25</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Tue Dec 06 21:47:02 2022 JST</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -380,7 +369,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.13"
+   "version": "3.9.7"
   }
  },
  "nbformat": 4,
diff --git a/docs/tutorials/09_application_classes.ipynb b/docs/tutorials/09_application_classes.ipynb
index cf4b76d44..7faab2d13 100644
--- a/docs/tutorials/09_application_classes.ipynb
+++ b/docs/tutorials/09_application_classes.ipynb
@@ -68,9 +68,10 @@
    "outputs": [],
    "source": [
     "from qiskit_optimization.algorithms import MinimumEigenOptimizer\n",
-    "from qiskit_aer import Aer\n",
-    "from qiskit.utils import algorithm_globals, QuantumInstance\n",
-    "from qiskit.algorithms import QAOA, NumPyMinimumEigensolver"
+    "from qiskit.utils import algorithm_globals\n",
+    "from qiskit.algorithms.minimum_eigensolvers import QAOA, NumPyMinimumEigensolver\n",
+    "from qiskit.algorithms.optimizers import COBYLA\n",
+    "from qiskit.primitives import Sampler"
    ]
   },
   {
@@ -95,10 +96,7 @@
     "import networkx as nx\n",
     "\n",
     "seed = 123\n",
-    "algorithm_globals.random_seed = seed\n",
-    "qins = QuantumInstance(\n",
-    "    backend=Aer.get_backend(\"qasm_simulator\"), shots=1000, seed_simulator=seed, seed_transpiler=seed\n",
-    ")"
+    "algorithm_globals.random_seed = seed"
    ]
   },
   {
@@ -118,9 +116,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -201,9 +199,9 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -222,7 +220,11 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "nbsphinx-thumbnail"
+    ]
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -234,14 +236,14 @@
       "\n",
       "solution: [0, 1, 3, 4]\n",
       "\n",
-      "time: 0.0762031078338623\n"
+      "time: 2.362724781036377\n"
      ]
     },
     {
      "data": {
-      "image/png": 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mszq//RbYtq1GL5Wbm4ugoCBxEd67dw/m5ubiIjQzM6MN7qRKu3btgre3NwIDA+mYnnIKCwthbm6OpUuXYsaMGazjyC0qPiIh7fRpaE2ejCYaGlCvYosDgNLjirS0SkedrV9f+us6yMrKQmBgoLgIk5KSYGVlJS5CU1NTOhGdAAB8fHwwa9YsREREoGPHjqzjyJ3bt2/D2dkZ4eHh6N69O+s4comKj4gJhUIMGjQIXxkbY2FxcekhtGpqkpc/yy5HDhwIfPcdYGsrkywZGRkICAgQF+Hz589ha2srLkIjIyOaVqGCHjx4ADs7O1y8eBE2Njas48itvXv34sKFC/Dz86MfGCtBxUfEduzYAR8fH/D5/NJSKSgAvL2BqCjg3bvSfXo9ewJTpgBffFGv2dLS0uDv7y8uwtevX8Pe3l5chH379lWp/VuqKCsrCxYWFli2bBlmz57NOo5cE4lEGDRoEJydnbF27VrWceQOFR8BAERGRmLEiBGIiopC586dWcf5pDdv3sDPz09chBkZGXBwcBAXoZ6eHr25r0SEQiFGjRqFrl274sCBA6zjKITk5GSYmprCx8cH/fv3Zx1HrlDxEeTk5MDExARbt27F+PHjWceplZcvX0oUYV5eHhwdHcVFqKurS0WowNasWYPQ0FDcuHGDDiGugTNnzmDjxo2IiYmh7UPlUPERuLu7Q11dHUePHmUdRWqeP38uLkE+nw+hUChRhN27d6ciVBBnzpzB6tWrERkZSfvTamHKlClo0aIFfvnlF9ZR5AYVn4o7e/Ysvv/+e8TExCjtZBWO4/DkyROJImzQoIG4BJ2cnBTi8q4qiomJwZAhQ3Dr1i0YGhqyjqOQ3r9/D0NDQxw6dAjDhw9nHUcuUPGpsOfPn8PMzEzl3gPgOA4PHz6UKEJtbW2JImzfvj3rmCovNTUVZmZm2LNnD8aNG8c6jkLz8/ODm5sb4uPj8fnnn7OOwxwVn4oSCARwdHTEyJEjsXLlStZxmOI4DomJieIS9Pf3R6tWrcQl6OjoiLZt27KOqVKKi4sxcOBAODg44IcffmAdRymsXLkSDx8+xMWLF1X+Mj8Vn4ravHkz/P39cfPmTdoG8AGRSITbt2+L54wGBgaiffv24iJ0cHCg95pkbN68eUhJScHFixfpz6eUFBUVwcLCAgsXLsTMmTNZx2GKik8FhYSEYPTo0YiOjkaHDh1Yx5F7QqEQsbGx4hVhcHAwunbtKi5Ce3t7tGzZknVMpeHp6Yn9+/cjNDQUzemMR6m6e/cuHBwcEBYWhp49e7KOwwwVn4rJysqCkZERPDw8MGrUKNZxFFJJSQmio6PFRRgaGgpdXV1xEdrZ2dFf2LUUEBCAcePGITg4WKX/YpYlDw8PnD17FoGBgSo71YWKT4VwHIfJkyejRYsWOHToEOs4SqO4uBgRERHiIoyIiIC+vr7EWYRNmzZlHVPuPX/+HJaWlvj9998xePBg1nGUlkgkwpAhQ2BnZ4f169ezjsMEFZ8KOXHiBLZv346oqCjazCpDhYWFCAsLExdhTEwMDA0NJc4ipBMFJOXn58PGxgZTp07F0qVLWcdReq9evYKxsTGuXLkCc3Nz1nHqHRWfinj8+DEsLS1pPxQDdBbhx3Ech0mTJqFhw4b4/fffVf6Ow/ry559/Yu3atYiNjVW5KxJUfCqgpKREfFL14sWLWcdReXQWoaQdO3bgwoUL8Pf3p5VwPZs2bRqaNGkCT09P1lHqFRWfCli3bh2io6Nx9epVujVcDqnyWYRXrlzBnDlzEBERQXcYM1B2s9v+/fvx5Zdfso5Tb6j4lJy/vz8mTpyIuLg42oStIFTlLMJ79+7BwcEBly5dgpWVFes4KiswMBDjx49HfHw82rRpwzpOvaDiU2IZGRkwMjKCp6cnzehTYOXPIuTxeHj79q3EWYQGBgYKt5J///49zM3NsXr1ari7u7OOo/JWr16Nu3fv4tKlSyrxHisVn5LiOA7jx49H+/btsW/fPtZxiBS9fv1a4gim9+/fS5xF2KdPH7n+y0soFOLLL7+Erq4u/dmUE8XFxbC0tMTcuXPx9ddfs44jc1R8Suro0aPYt28fIiIi0LhxY9ZxiAy9fPlSYuB2QUGBxBFMOjo6clWEq1atQlRUFK5du0Zn68mRxMREODg4ICQkBDo6OqzjyBQVnxJ68OABbGxs4O/vD319fdZxSD179uyZRBGKRCKJkye6devGrAj/+OMPrF+/HhEREWjVqhWTDKRq+/fvx8mTJxEUFKTUP5RQ8SmZ4uJiWFlZYdasWZg3bx7rOIQxjuPw+PFjiSJs2LAhk7MIo6KiMGzYMPB4PPTt27deXpPUjEgkwvDhw2FpaYmNGzeyjiMzVHxKZuXKlXjw4AH++usvubq8ReQDx3F48OCBuAT9/Pzq5SzCN2/ewNzcHB4eHhg9erTUn59IT0pKCoyNjXHp0iVYWlqyjiMTVHxK5NatW5gxYwbi4uLo2BxSLRzH4e7duxJnEbZu3VqqZxEWFRXB2dkZgwYNUupVhDK5cOECVq5cibi4ODRr1ox1HKmj4lMS7969g5GREby8vDBw4EDWcYiCEolESEhIEBfhh2cROjo61ui9OY7j8PXXX+Pdu3c4f/68wm27UGXu7u7Q1NTE4cOHWUeROio+JcBxHFxdXdGrVy/s2rWLdRyiRD51FqGDgwP+85//VPn9Bw8exMGDBxEaGgptbe36C07qLDs7G0ZGRvjpp5+U7ggzKj4lcOjQIRw5cgShoaEqNeOR1L+anEXo5+eHiRMnIiQkBN27d2ecnNRGcHAwxowZg7i4OLRr1451HKmh4lNwd+/ehaOjI4KCgtCrVy/WcYiKqeosQhMTE5w9exbHjx9XqRmQymjdunWIjY3FlStXlOaGOSo+BVZYWAhzc3N8++23mDlzJus4hKCwsBA8Hg/u7u5o2rQpUlNTYWRkJF4RWllZ0QkMCqakpARWVlaYOXOm0myRouJTYIsXL8arV6/g7e2tND+JEcVWNiqvWbNm+O2331BQUIDg4GDxivD27dvo37+/uAgtLCxU+ixCRXH//n3Y2dkpzZUlKj4F5ePjg7lz5yI+Ph4tW7ZkHYcQAMDWrVvx999/w8/Pr9JReTk5ORJnEd6/fx8WFhYSZxEq88QQRXbw4EEcO3YMISEhCv/viIpPAb19+xZGRkY4c+YMHBwcWMchBABw+fJlzJ8/HxEREdXeBJ+VlSVxBNPjx49hbW0tLkITExOlPYtQ0XAchxEjRsDU1BQ//PAD6zh1QsWnYEQikfgP35YtW1jHIQRA6YBjR0dHXLlyBebm5rV+noyMDPERTHw+H8nJyRJnERoaGirFWYSK6s2bNzAyMsKFCxdgbW3NOk6tUfEpmH379uH06dMIDAxU+MsNRDlkZGTAwsIC33//PaZNmybV505NTZUoQmU4i1DR/fXXX1i2bBni4uIUdm8mFZ8CiY+Px8CBAxEWFoYePXqwjkMIBAIBhg8fDgMDA+zdu1fmr6foZxEqi1mzZkEkEuG3335jHaVWqPgURH5+Pvr374/Vq1dj6tSprOMQAgBYvnw54uPj8c8//zB5Ly45OVmiCOX9LEJlkZOTAyMjI+zevVshh45T8SmIefPmITs7GydPnqT/kIlcOHHiBDZt2oSIiAh89tlnrOMAqHgWIcdx4iJ0dnZGt27dWEdUGqGhoXB1dUVcXBy++OIL1nFqhIpPAVy6dAmLFy9GXFwcWrRowToOIYiIiMCXX34JPp8vt4cdV3YWYaNGjSSOYOrUqRPrmApt/fr1iIyMhI+Pj0L9QE7FJ+devXoFExMTXLx4UaHvoiLK4/Xr1zA3N8eBAwcUanhxZWcRNm/eXKIIFW3lwlpJSQlsbGwwffp0LFiwgHWcaqPik2MikQiDBg2Cg4MD1q9fzzoOISgqKoKjoyOGDx+O77//nnWcOhGJREhMTJQ4i/Dzzz+XOIKpTZs2rGPKvYcPH8La2hqBgYHo06cP6zjVQsUnx3bt2oW///4bfD6fNvES5jiOw8yZM5GdnQ1vb2+l20YgEokQHx8vcRZhx44dJY5gqslZhKrk//7v//Drr78qzAkxVHxyKioqCsOHD0dkZCS6dOnCOg4h2L9/Pw4fPoyQkBClPJX7QwKBoMJZhN27dxcXob29/UfPIlQlHMdh5MiR6Nu3L7Zt28Y6zidR8cmh3NxcmJiYYPPmzZg4cSLrOISAx+PBzc0NoaGhKntnZElJCaKiosRFGBYWhl69ekmcRaioG7qloWyUore3N+zs7FjH+SgqPjk0c+ZMiEQiHDt2jHUUQvDkyRNYW1vj9OnTcHJyYh1HbhQVFUmcRRgZGQkDAwNxEdrY2KBp06asY9arv//+G4sWLUJ8fLz4QGJ5RMUnZ/7880+sWbMGMTExKv3TI5EPubm5sLKywpw5c/DNN9+wjiPXCgsLERoaKi7C2NhYlTyLcM6cOSgqKoKXlxfrKFWi4pMjL168QP/+/XH16lWYmZmxjkNUnEgkwtixY/HZZ5/h8OHDCrVPSx7k5eUhJCRE5c4izM3NhbGxMbZv346xY8eyjlMpKj45IRQK4eTkhOHDh+O7775jHYcQbN68GdeuXRNv/CZ1o0pnEYaHh2PkyJGIiYlBhw4dWMepgIpPTmzZsgU8Hg83b96kY1cIc3/99RcWLlyIiIgI2tQtI+/fv0dgYKDSnkW4adMmBAcH49q1a3K39YWKTw6UzbyLjo5Gx44dWcchKu7OnTtwcnKCj48PXXKvRx87i9DZ2RmGhoZyVyAfIxAIYGtrCzc3NyxatIh1HAlUfIxlZ2fDyMgIP/74I7766ivWcYiKS09Ph7m5OTZt2oQpU6awjqPSUlNTJU6eSE1NlTiCSV9fX+6LMCkpCZaWlvD395erma5UfIxNnToVTZo0wf/93/+xjkJUnEAgwNChQ2FsbIzdu3ezjkM+kJKSIlGEWVlZEkcw9e7dWy5vQDpy5Ah++eUXhIWFyc17xVR8DJ08eRJbt25FVFSUyu33IfJnyZIlSExMhI+PD73PrACSk5MlTp4om6NaVoQ9e/aUiyLkOA6urq7o3bs3du7cyToOACo+Zp48eQILCwvcuHEDxsbGrOMQFefl5YWtW7ciIiICLVu2ZB2H1MLTp08lihCAxMkTLCfupKWlwdDQEKdPn4aDgwOzHGWo+BgQCASws7PDuHHjsHTpUtZxiIoLDw+Hi4sL/P39FWa6Pvk4juOQlJQkUYSNGzdmehbh1atXMX/+fCQkJDA/V5SKj4H169cjPDwc//zzj9y/OU2UW0pKCszNzXHo0CG4uLiwjkNkhOM43L9/X+IswhYtWtT7WYTz589HTk4OTpw48b9PvngB3L8PZGcDzZoB3bsDuroyzUHFV88CAgIwYcIExMbGol27dqzjEBVWWFgIBwcHjBo1CmvWrGEdh9QjkUiEu3fvSpxF2KZNG5mfRZifnw9jY2P8sHEjxjdvDuzcCURGAo0aARwHqKkBxcWlxbdqFTBmDCCDY46o+OpRZmYmjIyMcPDgQYwYMYJ1HKLCOI6Du7s78vPzcfbsWbm4CYKwIxQKkZCQUC9nESZcvgztr75CFy0tqOflVf1AbW2gcWPgxg3AyEgqr12Giq+ecByHiRMnok2bNti/fz/rOETFeXh4wMvLC8HBwXRHMalAZmcRJiUBFhYQZmZCo7rV07QpcOsWYGlZ89erAhVfPTl27Bj27t2LiIgIlZjQTuTXrVu3MHXqVISGhqJr166s4xAFUNVZhM7OznBycoKtre2nT5PJygL09YHXrwGRqGYBWrQA4uMBKR3KTcVXDx4+fAgbGxvw+XwYGBiwjkNUWNk8SG9vb7m4rZwopsrOIuzbt6/EWYRNmjSR/KY9e4D164GCgkqf8xGAvgDGAjj54Rc1NYGZMwFPT6nkp+KTseLiYlhbW8Pd3R0LFixgHYeosJycHFhZWWHBggWYN28e6zhEiRQUFEicRRgXFwdjY+P/nUVoYYHGPXuWrvaqMBhAAYAuqKT4AKBJEyA1tfTSZx1R8cnYd999h7t37+Ly5ct0AwFhRiQSYfTo0Wjbti08PT3pzyKRqby8PAQHB4uLsE1cHM4IBGgiFFb6+DMALgDQA5CEKoqvaVPgxx+BOXPqnI+KT4Z8fX0xbdo0xMXF4fPPP2cdh6iwDRs2wNfXFzweDw1lcHs4IR9TtHw5Gv74Iyr7cSsbQH8APABH8JHiAwBXV+DixTrnUdzDnuRceno6pk+fjmPHjlHpEabOnz+PY8eOITIykkqPMNEoK6vKr30PYCaAah3I9u6dVPJQ8ckAx3GYNWsWJk6ciMGDB7OOQ1RYQkIC5s6di2vXrqFt27as4xBVVcWpDHEAbgGIrePz1BQVnwz8+uuvePbsGc6cOcM6ClFh7969g6urK37++WeYmpqyjkNUWadOQIMGQEmJxKf9ADwD0PnfX+cCEAJIBBDz4XOoqZU+jxTQe3xSlpiYCHt7ewQFBaF3796s4xAVVVJSgiFDhsDc3Bw7duxgHYeousePAQMDoLBQ4tP5KH2Pr8welBbhIQAV3iBq1gy4cgWQwjYcmpAsRUVFRXBzc8P27dup9AhTy5YtQ+PGjbF161bWUQgBevQAzMwqfLoJgHblPpoBaIxKSg8APvsMsLeXShy61ClFq1evRo8ePTBr1izWUYgKO3r0KK5fv47w8HA6UJbIj5UrgZgY4CPzOTdW9YUmTYAVK0ovd0oBXeqUkmvXrmH27NmIj4/HZ599xjoOUVEhISFwdXVFQEAAXXUg8oXjgMmTIbhwAZpFRdX/vkaNSleLPF7p+4RSQJc6peDt27f473//i+PHj1PpEWZevnyJcePGwcvLi0qPyB81NYTPn4+bIhGEjRtX73u0tIB+/YCrV6VWegAVX51xHIf//ve/mDFjBpycnFjHISqqoKAAX331FRYtWoThw4ezjkNIBffu3cOosWMhOn8eGmvWAM2bl96wUpmmTUsvb86cCQQGlj5WiuhSZx3t378fJ06cQHBwMBpI8ScSQqqL4zhMmzYNAoEAp06donFkRO68fPkStra22LRpE6ZPn176yaIi4MIFYPfu0uOKCgpKL2t27AgsWQJMnlx1MdYRFV8dJCQkYMCAAQgNDUXPnj1ZxyEq6scff8Qff/yBoKCgihPxCWEsMzMTdnZ2mDZtGlauXMk6DgC6q7PWCgoKMGnSJOzZs4dKjzBz/fp17NmzB+Hh4VR6RO4UFBTAxcUFgwcPxooVK1jHEaMVXy0tWLAAGRkZdGmJMPPo0SPY2tri3LlzsLOzYx2HEAkCgQBjxoxBs2bNcOLECairy88tJbTiq4W///4bV69eRVxcHJUeYSI7OxujRo3C5s2bqfSI3OE4DnPnzkVhYSH+/PNPuSo9gFZ8NZaSkgITExOcO3cOtra2rOMQFSQSiTBq1Ch06tQJBw8eZB2HkArWrVuHGzdugMfjoZmMblCpC1rx1YBIJML06dMxd+5cKj3CzPr165GdnQ0PDw/WUQipYP/+/fD29kZwcLBclh5AxVcje/fuRX5+PtatW8c6ClFR3t7eOHnyJCIiIuhsPSJ3zp49i507dyIoKEiuzyGlS53VFBMTgyFDhiAyMhJdu3ZlHYeooLi4OAwaNAg3btyAsbEx6ziESLh16xbc3Nxw69Yt9OvXj3Wcj5KvdxzlVF5eHiZNmoSff/6ZSo8wkZaWhq+++goHDhyg0iNyJyYmBm5ubjh37pzclx5AK75qmT17NoqLi/H777+zjkJUUElJCQYNGgQbGxs6ZojInaSkJNjb2+PgwYNwdXVlHada6D2+Tzh//jz4fD5iY2NZRyEqavHixdDW1sYPP/zAOgohEt68eYMhQ4Zg48aNClN6ABXfRyUnJ2P+/Pm4fPkytLW1WcchKujXX38Fj8dDWFiY3O2FIqotKysLQ4cOxYwZM/D111+zjlMjdKmzCkKhEAMGDMDgwYOxZs0a1nGICgoKCsLo0aMRFBQEXV1d1nEIESssLMSwYcOgp6eHAwcOKNwgDyq+Kmzbtg03btyAr68vnWJN6l1ycjIsLCzw22+/YejQoazjECImFAoxceJEAMCZM2cU8u9HutRZifDwcOzbtw9RUVEK+S+VKLb8/Hy4urpiyZIlVHpErnAch0WLFiE9PR3//POPwv79SCu+D+Tk5MDY2Bg7d+7EmDFjWMchKobjOEyePBnq6uo4ceKEwl1CIsrthx9+wIULF+Dn54cWLVqwjlNrtOL7wDfffAMnJycqPcLE7t278fDhQwQGBlLpEbny66+/wsvLC8HBwQpdegAVn4RTp04hLCwMMTExrKMQFeTj4wMPDw9ERERAS0uLdRxCxC5evIiNGzciICAA7dq1Yx2nzuhS57+ePn0Kc3NzXL9+HSYmJqzjEBXz4MED2NnZ4eLFi7CxsWEdhxCxgIAAjB07Fv/88w9MTU1Zx5EK2hiE0gMTp0yZglWrVlHpkXqXlZWFUaNGYevWrVR6RK4kJCRg3LhxOH36tNKUHkArPgDAxo0bERwcjOvXr9MmYVKvhEIhRo4ciW7duuHAgQOs4xAi9uzZM9ja2mLv3r0YP3486zhSpfLv8QUFBcHT0xOxsbFUeqTerVu3Dvn5+fjpp59YRyFELC0tDUOGDMGqVauUrvQAFS++9+/fY8qUKTh8+DC++OIL1nGIMrlzB+DzgcxMQFMTaNMGGDmy9H//dfr0aZw5cwaRkZFo0KABw7CE/E9ubi5GjBiBcePGYeHChazjyITKXurkOA6TJk1Cq1at8Msvv7COQ5RBSQlw4QKwYwfw4AHAcUBREaCuDjRuDAiFwNChwPLliNHSwpAhQ3Dr1i0YGhqyTk4IAKC4uBguLi7o1KkTDh8+rLRbalS2+H7//Xfs2rULUVFRdOs4qbt374CBA4HHj4Hc3Kofp6YGUePGOK2hgYZHjmDchAn1l5GQjxCJRJg6dSpyc3Nx/vx5aGoq7wVBlSy+pKQkWFlZwdfXVyEOTSRyLjMTMDEBUlKA4uJqfUtxgwZo6OoKnD0LKOlP1URxcByHpUuXIioqCjdu3FD6xYDK3c1RUlICNzc3rF+/nkqP1B3HASNG1Kj0AKBhSQlw9SpAB8sSObB7927cvHkTly9fVvrSA1Sw+DZs2IDPP/8c33zzDesoRBlERAAJCZWW3hQAXwBoDkAXwJEPH5CfD+zaBRQWyjwmIVXx8vLCwYMHcf36dbRs2ZJ1nHqhUsXH5/Ph5eWFY8eOKe2btqSe7dkDFBRU+qXVAJ4ByAZwGcA6ANEfPojjgHPnZBiQkKpdvXoV3333Ha5du4YOHTqwjlNvVKb40tPTMW3aNPz2229oU+6WckJqLSMDuHIFEIkq/bI+gEb//rPavx+PP3xQbm7pqo+QehYaGooZM2bg0qVL6N27N+s49Uolio/jOMyePRvjxo2j882I9Dx8CDRs+NGHzAfQBEBvlF72HF7Zgx48kHo0Qj4mMTERX331FY4fPw4LCwvWceqd8t6vWs6RI0fw5MkTnD59mnUUokyysj55R+ZBAPsBhALww/9WgBJKSgCBoHSjOyEylpycjGHDhmH37t0YNmwY6zhMKP2K7/79+1i9ejVOnTqFRo0q/WuHkNpp0gTV2QukAcAWwEsAhyp7gLo6oKAnWRPFkpGRgaFDh2LRokWYOnUq6zjMKPWPmEVFRZg0aRK2bt0KPT091nGIEsjNzUV4eDiCg4Px2NcXnllZqO7N3wJU8h4fALRuTXv5iMzl5+fDxcUFw4cPx7Jly1jHYUqpi2/t2rXo2rUrvv76a9ZRiIJ68eIFQkJCEBwcjODgYDx8+BBGRkawsbHBmGXLoPn+fel2hg+kAuAB+BKAFoBbAE7/+yGhcWNg/nzZ/iaIyhMIBJgwYQJ69OiBnTt3so7DnNIW340bN3DmzBnEx8fT1gVSLQKBAPHx8RJFV1RUBBsbG9jY2MDNzQ0mJiaSl8wLC4GZMyuMKVND6WXNuQBEALoA8AAwsrIXph/MiAxxHIevv/4aQqEQR48epVNooKQjy1JTU2FsbIzjx49jwIABrOMQOZWVlYXQ0FBx0UVGRqJz586wtrYWl12PHj0+/oNTcTHQoUPprM6aatiwdOrLhQu1/00Q8glr1qyBr68veDwemjZtyjqOXFC64uM4DiNHjoS+vj527NjBOg6RExzH4enTp+KVXEhICJ4+fYr+/fvDxsYG1tbWsLKyqt3kiqgowMGhdBJLdWlqlhZmXBzwn//U/DUJqYZ9+/bh0KFDCAoKQuvWrVnHkRtKV3y//PILvLy8EBwcjIaf2GNFlFdxcTFiY2Mlik5dXV28krO2toaRkZH0zsHz8wNcXErLr4oN7eJs6upo2K0b4O9fWn6EyMCZM2ewYsUKBAUFoUuXLqzjyBWlKr47d+7AyckJISEh0NHRYR2H1KP09HSEhISIL1vGxMSgZ8+eEkXXpUsX2b7f++ABsHZt6TQXdfWKo8yaNQOnpobDAHROnIDTqFGyy0JU2s2bNzFlyhT4+vrCwMCAdRy5ozTFV1BQAHNzcyxduhTu7u6s4xAZ4jgODx8+FK/kgoODkZKSAgsLC/H7cxYWFmjevDmbgGlpwOHDpQWYkVF6WbNt29KbYMaMwd83bmD58uVISEigvaVE6iIjIzF8+HBcuHABdnZ2rOPIJaUpvoULFyI1NRVnzpyhuziVTGFhIaKioiQuWzZr1ky8krOxsUHfvn2hoUCbwF1dXWFqaorvv/+edRSiRB4+fAgHBwd4enpiFF1RqJJSFN/Vq1exYMECxMbGqsyxGsrs7du3ElsKEhISoKenJ3HZUtEnyb948QImJiYIDw9Hjx49WMchSuD169ewsbHBmjVrMGvWLNZx5JrCF9/r169hYmICb29vWtYrIJFIhMTERImiS09Ph5WVlbjozMzMlPI27F27doHP58PHx4euUpA6ycrKgr29PcaPH4+1a9eyjiP3FLr4RCIRhg0bBgsLC2zevJl1HFINeXl5iIiIEF+yDA0NRevWrSX2zvXp00clNtmWlJTAyMgImzZtwtixY1nHIQqqsLAQQ4cORb9+/bBv3z76IaoaFLr49u7di3PnziEgIACaNNleLr169UrivbnExEQYGhqKi87a2hpt27ZlHZOZgIAATJ48GYmJidDW1mYdhygYoVCI8ePHQ1NTE6dPn1aJHxilQWGLLzY2FoMHD0ZERAS6devGOg5B6X+ECQkJEpct8/PzJUquf//+aNy4MeuocmXGjBlo1aoVfvzxR9ZRiALhOA7z5s1DUlISrl69SncI14BCFl9eXh769++PdevWYfLkyazjqKzs7GyEhYWJiy48PBwdOnSQuAlFV1eXLr18QlpaGvT19XHr1i3069ePdRyiIDZt2oTLly+Dz+ez27qjoBSy+ObMmYP8/HycOHGCdRSVwXEcnj9/LnHZMikpCaampuIVnZWVFVq1asU6qkL69ddf4eXlhaCgILpcRT7J09MTe/bsQXBwsEq/VVBbCld8Fy9exPLlyxEbG0s/5chQSUkJ4uLiJIpOJBJJ7J0zNjamsXBSUvb/7cyZM+lWdPJR58+fx6JFixAQEEBbYWpJoYrv5cuXMDU1xaVLl2Bpack6jlLJzMxEaGiouOiioqLQvXt3iaLr1q0bXbaUobi4OAwZMgR3796lgcKkUn5+fhg/fjyuX78OY2Nj1nEUlsIUn1AoxKBBg+Ds7Ix169axjqPQOI5DUlKSxMivFy9ewNzcXPz+nIWFBf5DpwbUu8WLFyMnJwdHjx5lHYXImfj4eAwaNAhnzpyBs7Mz6zgKTWGKb8eOHfDx8QGfz1eo0VTyoKioCNHR0eKiCwkJQcOGDcUlZ2Njg379+tGWEDmQnZ0NPT09nD17FjY2NqzjEDnx9OlT2NnZ4aeffsK4ceNYx1F4ClF8kZGRGDFiBKKiotC5c2fWceReWlqaxJaCuLg49O7dW2KTeKdOnVjHJFXw9vbGDz/8gJiYGOkdm0QUVmpqKmxtbbF48WLMnz+fdRylIPfFl5OTAxMTE2zbto1+0qmESCTC/fv3JYouNTUVlpaWEicVNGvWjHVUUk0cx2Ho0KEYNGgQli9fzjoOYSgnJwdOTk4YPnw4TaeSIrkvPnd3d6irq9N7Hv/Kz89HZGSkxMivFi1aSOyd09fXp8vBCi4pKQmWlpaIjY2l1bmKKi4uxogRI9C9e3d4enrSjWVSJNfFd+bMGaxfvx4xMTEqu2J5/fq1xJaCO3fuwMDAQKLovvjiC9YxiQxs2rQJ8fHxuHDhAusopJ6JRCJMnjwZRUVF+PPPP+kHWSmT2+J7/vw5zMzM8M8//8DU1JR1nHohFApx584dicuW2dnZsLa2Fl+2NDMzg5aWFuuopB4UFhaib9++8PDwwIgRI1jHIfWE4zgsXrwYcXFxuH79Oo34kwG5LD6BQABHR0eMHDkSK1euZB1HZnJychAeHi4uurCwMLRr105i71yvXr1okocKu3HjBubOnYs7d+6gSZMmrOOQerBjxw6cOnUKAQEBtKVIRuSy+DZv3gx/f3/cvHlTqf7Sf/HihcTeuQcPHsDY2FhcdNbW1vj8889ZxyRyZsKECdDR0cGWLVtYRyEyduzYMWzevBnBwcFo37496zhKS+6KLzg4GGPGjEF0dLRCn7ItEAgQHx8v8f5cUVGRxN45ExMTmqhOPiklJQWGhoYIDAxE7969WcchMvL333/j66+/hr+/P3R1dVnHUWpyVXxZWVkwMjKCh4cHRo0axTpOjbx//x5hYWHioouMjETnzp0liq5Hjx50ZxaplX379uHSpUvw9fWlP0NKKCQkBK6urrhy5QrMzc1Zx1F6clN8HMdh8uTJaNGiBQ4dOsQ6zkdxHIcnT55IXLZ89uwZ+vfvL3FSQcuWLVlHJUpCIBDAzMwMy5cvp6O4lMzdu3fh7OyM48ePY8iQIazjqAS5Kb4TJ05g+/btiIqKkrs38YuLixETEyNRdBoaGhJbCoyMjGjKBpGpsLAwjB49GomJiXTTg5JITk6GjY0Ntm/fTj/Q1CO5KL7Hjx/D0tISt27dgqGhIes4SE9PFxdcSEgIYmJi0LNnT4mi69KlC11yIvVuzpw5aNCgAQ4cOMA6Cqmj9PR02NnZYfbs2ViyZAnrOCqFefGVlJTA1tYWbm5u+Pbbb+v99TmOw4MHDyT2zqWkpMDCwkLipAI6+4/Ig4yMDOjp6eHKlSvo378/6ziklvLy8jBw4EDY29tj586drOOoHJkWX2ZmJh4+fIi3b99CIBBATU0NjRo1QteuXdG9e3c0btwY69atQ3R0NHx8fOplBVVQUICoqCiJFV2zZs0k9s717duXJiUQufX7779j//79CA8Ppz+nCqikpARfffUVWrdujWPHjtGVIwZkUnypqamIiYlBXl4eRCIRPnwJDQ0NcByHBg0aYMWKFQgMDETbtm2lHQMA8PbtW4ktBQkJCdDT05MoOkXeNkFUD8dxcHR0xPjx47FgwQLWcUgNcBwHd3d3vHv3DhcvXqT7AhiRevE9ffoUMTExEAqFn3ysUCiEpqYmhg4dCm1t7Tq/tkgkQmJiokTRpaenw8rKSlx05ubmaNq0aZ1fixCW7t69C0dHR9y+fRvt2rVjHYdU06pVqxAQEABfX1+5u4lPlUi1+F69eoWwsLBqlV55jRo1wuDBg2s8gzIvL6/CyK/WrVtLnDvXp08fpZr+QkiZVatW4dWrVzh58iTrKKQa9u7diyNHjiAwMBCtWrViHUelSa34SkpKcPny5QqlV1JSgqNHj+L27dvIzc1F27ZtMWnSJBgbG/8vhJoa2rZtC3t7+4++xsuXLyW2FNy7dw+GhobiorO2tpbZJVNC5E1eXh709PRw7NgxODs7s45DPuKPP/7A6tWrERwcTMdMyQFNaT3Rs2fPKv28UChEq1atsGHDBrRu3RqxsbHw8PDA7t270aZNGwCl171TU1ORn58vXv4LBALcvn1boujy8/PFJefh4YH+/fvT5HKispo2bYqff/4Z8+fPR3x8PI2/k1PXr1/H0qVLwePxqPTkhFRWfBzH4erVq8jPz6/W41esWIGxY8fCwsJC/Dl1dXU0aNBAvFE8IiICHTp0kNg7p6urS3dAEfKBkSNHwsLCAmvXrmUdhXwgIiICX375Jf766y9YW1uzjkP+JZUVX1ZWFoqKiqr12Pfv3+P169fo2LGjxOdFIhEyMjJQXFyMxYsXw8rKiq6DE1INP//8M/r3749Jkyahe/furOOQfz148ACjRo3C0aNHqfTkjFSKr7CwEOrq6p+8qUUgEGD//v2wt7evdAuBtrY2tm/fLo1IhKiMrl27Yvny5Vi4cCGuXLlCV0XkQEpKCoYOHYpt27bBxcWFdRzyAanc7liduzhFIhEOHDgATU1N/Pe//630MXIwPY0QhbR06VI8ffoUf/31F+soKu/9+/cYOnQo5syZA3d3d9ZxSCWkUnwNGzb86Nc5joOnpyeysrKwbNkyaGpWvtCkbQeE1E7Dhg1x8OBBfPvtt8jNzWUdR2UVFBRg5MiRcHZ2xqpVq1jHIVWQStO0aNHio6u+I0eO4NWrV1i1alWVJclxHO7cuYO+ffti0aJFuHjxIjIyMqQRjxCV4OjoCEdHR2zatIl1FJUkFArh5uaGjh07Yu/evXTJWY5JbR9fWFgYkpOTK1yuTEtLwzfffIMGDRpIrOhmz54NOzs78a81NTVhYWGBV69egc/ng8/nIyQkBD179oSTkxOcnJxgb29Pw6IJ+YjU1FQYGBjA19cXffv2ZR1HZXAch7lz5+LJkye4evXqJ6+CEbakVnyZmZng8Xg1ntpSpnHjxnBxcZH4Kam4uBiRkZHg8/ng8XiIiIiAvr6+uAhtbW1p/BghH/D09MTJkycREBBAbx/Ukw0bNsDHxwc8Hk8q4xeJbEl1ZFlAQABSU1MhEolq9H0aGhro378/unTp8tHHFRYWIiwsTFyEsbGxMDIyEhehlZVVjceeEaJshEIhrK2tMWfOnCpvJCPSc/DgQXh4eCAoKEg8lIPIN6kWn0AggK+vL3JycqpdfhoaGtDV1a3VZZm8vDyEhISIi/DOnTswMzMTF6GFhQVdciAqKSYmBsOGDUNiYiLth5Whc+fOYfHixQgMDES3bt1YxyHVJPXTGQQCAYKCgpCenv7Ry55ll2D69esHXV1dqbx2dnY2goKCxEX48OFDWFlZwcnJCc7OzjA1Na3yjlJClM2iRYtQUFCAw4cPs46ilHg8HiZOnIibN2/C0NCQdRxSAzI5j4/jOKSnp+P+/ft48+YN1NXVwXGc+P07NTU19OjRAz179pTp0RyZmZkICAgQF+Hz589ha2srLkJDQ0M6yJMoraysLOjp6eHPP/+kySFSFhsbiyFDhuDPP/+Eg4MD6zikhmR6AjtQ+r5ceno6SkpKoK6ujkaNGqF169ZMCictLQ3+/v7g8Xjg8/l4+/Yt7O3txUWor69PNwMQpXLmzBls374d0dHRdLVDSh4/fgw7OzscOHAAo0ePZh2H1ILMi0+evX79Gn5+fuIizMrKgqOjo7gIe/XqRXtxiELjOA6DBw/GsGHDsHTpUtZxFN7bt29hY2OD5cuXY+7cuazjkFpS6eL7UHJysviyKJ/PR3FxsfhGGWdnZ3Tv3p2KkCichw8fwtraGnFxcRWGw5Pqy87OhqOjI0aNGoUNGzawjkPqgIqvChzH4enTpxJFqKmpKS5BJycndO7cmXVMQqpl/fr1SExMxLlz51hHUUhFRUUYMWIEdHR0cPDgQfoBWMFR8VUTx3F4+PChuAj9/Pygra0tUYRffPEF65iEVKqgoAAGBgY4cOAAhg0bxjqOQhGJRJg0aRIEAgG8vb3phjglQMVXSxzH4e7du+LVoL+/P9q0aSMuQkdHR3z++eesYxIidu3aNSxYsAB37tyhQQ/VxHEcFi1ahNu3b+PatWto3Lgx60hECqj4pEQoFCIhIUFchIGBgejcubO4CB0cHNCyZUvWMYmKGzduHPr06YPNmzezjqIQtm3bBm9vb/j7+6NFixas4xApoeKTEYFAgJiYGHERhoSEQEdHR1yEdnZ2NHCb1LtXr17B0NAQISEhUhscoayOHDmCbdu2ITg4mN7GUDJUfPWkuLgYERER4pMnygZul70/aGNjQwO3Sb346aefcPXqVdy8eZNu0qjC5cuXMXfuXPj7+0NHR4d1HCJlVHyMlA3cLlsRlg3cLitCKysrej+ByIRAIICpqSm+++47TJo0iXUcuRMUFITRo0fjn3/+gampKes4RAao+ORE2cDtsiIsG7hdVoTm5uY0cJtITUhICMaNG4fExER676qcO3fuYMCAATh58iQGDRrEOg6RESo+OZWdnY3AwEDxpdGygdtlRUgDt0ldzZ49G1paWvj5559ZR5ELZbN8d+/ejYkTJ7KOQ2SIik9BZGZmwt/fX1yEZf+RlhUhDdwmNZWeng59fX34+PjAxMSEdRym3r17Bzs7O8ybNw+LFi1iHYfIGBWfgkpLS4Ofn5+4CMsGbpcVIQ3cJtVx7NgxHDp0CKGhoSr7g1NeXh4GDBgAZ2dnbNu2jXUcUg+o+JRESkqKRBFmZWWJ54w6OTnRwG1SKZFIBAcHB7i5uWHevHms49S7kpISjBw5Eu3bt8eRI0fovxEVQcWnpF68eCEuQT6fj5KSEokipIHbpMzt27fh7OyMO3fuoG3btqzj1BuRSITp06fj/fv3uHjxIr1nrkKo+FQAx3F48uSJRBFqamqKL4vSwG2yYsUKvH37FsePH2cdpd6sWLECISEhuHnzpkwPxCbyh4pPBXEchwcPHohLsGzgdvkipEkVqiU3Nxd6eno4fvw4HB0dWceRuT179uDYsWMIDAzEZ599xjoOqWdUfAQikQh3794VF2HZwO2yIqSB26rh4sWLWLt2LeLi4pR6z+iJEyewbt06BAcH0/mEKoqKj1QgFAoRHx8vLsLAwEB06dJFvBqkgdvKieM4uLi4wMbGBqtXr2YdRyb++ecfuLu7g8/no0+fPqzjEEao+MgnCQQCREdHi4uwbMBxWRHSwG3l8fTpU5iZmSEyMhLdunVjHUeqwsPD4eLigkuXLsHKyop1HMIQFR+pMRq4rdy2bt2KsLAwXL58WWnu/L1//z6cnJxw9OhRDB8+nHUcwhgVH6mzwsJChIaGioswNjYWxsbG4hUhDdxWLEVFRTA0NMTOnTsxatQo1nHq7NWrV7CxscHmzZsxbdo01nGIHKDiI1KXl5eH4OBg8Pl88Hg83L17F+bm5uIipIHb8o/H48Hd3R2JiYkKvXrPzMyEvb09pk2bhhUrVrCOQ+QEFR+RufIDt3k8Hh49egRra2txEdLAbfk0ZcoUdOjQATt37mQdpVYKCgowePBgmJubY8+ePUpz2ZbUHRUfqXcZGRkICAgQF+GLFy9gZ2cnLkIauC0f3r59CwMDA/D5fBgYGLCOUyMCgQBjxoyBtrY2jh8/TnNriQQqPsJcamqq+OQJHo+H1NRUODg4iIuQBm6zc/DgQZw5cwb+/v4Ks2LiOA5ff/01kpOTcfnyZbqsTiqg4iNyp/zAbR6Ph5ycHDg6OtLAbQaEQiEsLS2xYMECzJgxg3Wcalm3bh1u3LgBHo+HZs2asY5D5BAVH5F7ZQO3y06nFwgENHC7HkVFReHLL79EYmKi3I/32r9/Pw4cOICgoCCaNkSqRMVHFEr5gdtlRdiwYUOJIqSB29L3zTffoKSkBP/3f//HOkqVzp49i2XLliEoKAhdu3ZlHYfIMSo+otDKD9zm8Xjw8/NDixYtJIqQBm7X3fv376Gnp4cLFy7A0tKSdZwKbt26BTc3N9y6dQv9+vVjHYfIOSo+olTKBm6XrQb9/f3Rrl07cQnSwO3aO3XqFHbt2oWoqCi52n4SExODoUOH4ty5c7C3t2cdhygAKj6i1MoGbpcVYVBQEA3criWO4zBw4EC4uLhg8eLFrOMAAJKSkmBvb4+DBw/C1dWVdRyiIKj4iEopG7hdVoShoaE0cLsG7t+/D1tbW8THx6NDhw5Ms7x58wY2Njb47rvvMHv2bKZZiGKh4iMqrWzgdlkRRkZGwsDAQFyENHC7onXr1uHRo0c4e/YsswzZ2dlwcHDAmDFjsG7dOmY5iGKi4iOknIKCAoSFhYmLMC4ujgZuf6CgoAD6+vrw9PTE4MGD6/31i4qKMGzYMOjp6WH//v20lYXUGBUfIR9RNnC7rAhp4HYpHx8ffPvtt7h9+3a9/iAgFAoxceJEqKmp4fTp0zTajtQKFR8hNZCVlSUeuM3n85GUlAQrKys4OTnB2dkZJiYmcnXHoyyNGTMGffv2xcaNG+vl9TiOw8KFC3Hv3j34+PigUaNG9fK6RPlQ8RFSBxkZGeI5o3w+H8nJybC1tRUXoaGhodLOGU1OToaxsTFCQ0Oho6Mj89fbsmULzp8/D39/f7oBidQJFR8hUpSamiqeM8rn85GWlgZ7e3txEerr6yvVe1J79uzBjRs3cP36dZn+vg4fPoydO3ciKCgI7dq1k9nrENVAxUeIDKWkpIhLkM/nSwzcdnZ2hq6urkIXYUlJCUxMTPD9999j/PjxMnmNv/76C/Pnz0dAQAB69uwpk9cgqoWKj5B69Pz5c4kiLD9w29nZGd26dVO4IgwODsaECROQmJgo9UuQAQEBGDt2LK5duwYTExOpPjdRXVR8hDDCcRweP34sUYRlA7ednZ3h5OSETp06sY5ZLTNnzoS2tjY8PDyk9pwJCQkYNGgQTp06hQEDBkjteQmh4iNETnAch/v374tLsPzA7bIilNf3t969ewd9fX1cu3YNxsbGdX6+Z8+ewdbWFnv37pXZJVSiuqj4CJFTIpEId+7cERdh+YHbzs7OcHR0ROvWrVnHFDt69CgOHz6MkJCQOt3JmpaWBltbWyxcuBDffPONFBMSUoqKjxAFIRQKERcXJy7C8gO3nZ2dYW9vz3Tgtkgkgq2tLaZPn445c+bU6jlyc3Ph7OyMIUOG4IcffpByQkJKUfERoqBKSkoQHR0tLsLyA7ednZ1hZ2cHbW3tes2UkJCAgQMH4s6dO2jTpk2Nvre4uBguLi7o3Lkzfv31V4W7yYcoDio+QpREUVERIiIixEVYNnC77P1BGxsbNGnSROY5li1bhvT0dHh5eZV+Ij4eiIkBsrIALS2gY0dg0CCg3Kg3kUiEadOmITc3F+fOnVOZ6TeEDSo+QpRUQUEBQkNDxUVYNnC7rAgtLS1lMmczJycHRn364Kq7O3r/9Rfw5AmgpgYIBICGBqCpWfrrefOABQvAdeiAZcuWITIyEjdu3ICWlpbUMxFSHhUfISoiNzcXwcHB4PP54PF4SExMhLm5ubgIzczMpDNw++lT5Jubg0tPR9OP/fXSqBGgro4ro0Zh9Z07CAgIoEOBSb2g4iNERZUfuM3j8ZCUlARra2txEdZq4PbTp0D//uDev4eaSFStb8lXU0PR9u1ouWpVLX4XhNQcFR8hBIDkwG0ej4eXL1/C1tZWXISfHLhdUAD07g28fAlUs/TEtLQAHx/A0bFOvwdCqoOKjxBSqfIDt3k8Ht69ewd7e3txEVYYuO3lBSxcCOTmSjzPAQBeAG4DmPTvP1fKwgIIC5P+b4SQD1DxEUKq5dWrVxJFmJubK54z6uTkBF1XV6jdv1/h+y4AUAdwHUABPlJ8jRsDcXFAr14y+h0QUoqKjxBSK2UDt3k8Ht5dv45zqan42GaJdQBe4iPFp6kJzJoFHDok7aiESKDiI4TUGefpCW7JEqgXFlb5mE8WHwAYGAC3b0s3HCEfoF2ihJA6U8vOhppAUPcnys6u+3MQ8gm1nyRLCCFlGjcu3ZwujechRMao+Aghddehg8QIslrr2LHuz0HIJ1DxEULqbvhwoIrbBQQACgEI//0o/PdzFWhrAwsWyCohIWJUfISQutPSAtzdgQYNKnxpCwAtADsAnPz3n7dU9hyamsDIkbJMSQgAuquTECItSUlAv36lE1xqSksLWLkS2LhR6rEI+RCt+Agh0tGzJ7BnD1DTo48aNSotzDVrZJOLkA9Q8RFCpGf+fGDDhtIVXHVoaQFGRsD169K5OYaQaqDiI4RI18qVwJ9/lm5Gb9Kk8m0O2tpAixbAkiVAQEDpPxNST+g9PkKI7MTGAj/9VDp8OiendJ9ep07AN98Arq60yiNMUPERQghRKXSpkxBCiEqh4iOEEKJSqPgIIYSoFCo+QgghKoWKjxBCiEqh4iOEEKJSqPgIIYSoFCo+QgghKoWKjxBCiEqh4iOEEKJS/h/oKDhVA/QcHgAAAABJRU5ErkJggg==\n",
+      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -250,7 +252,7 @@
    ],
    "source": [
     "# QAOA\n",
-    "meo = MinimumEigenOptimizer(min_eigen_solver=QAOA(reps=1, quantum_instance=qins))\n",
+    "meo = MinimumEigenOptimizer(min_eigen_solver=QAOA(reps=1, sampler=Sampler(), optimizer=COBYLA()))\n",
     "result = meo.solve(qp)\n",
     "print(result.prettyprint())\n",
     "print(\"\\nsolution:\", prob.interpret(result))\n",
@@ -347,13 +349,13 @@
       "\n",
       "solution: [0, 1, 3]\n",
       "\n",
-      "time: 4.045089960098267\n"
+      "time: 2.2142601013183594\n"
      ]
     }
    ],
    "source": [
     "# QAOA\n",
-    "meo = MinimumEigenOptimizer(min_eigen_solver=QAOA(reps=1, quantum_instance=qins))\n",
+    "meo = MinimumEigenOptimizer(min_eigen_solver=QAOA(reps=1, sampler=Sampler(), optimizer=COBYLA()))\n",
     "result = meo.solve(qp)\n",
     "print(result.prettyprint())\n",
     "print(\"\\nsolution:\", prob.interpret(result))\n",
@@ -530,7 +532,7 @@
     {
      "data": {
       "text/html": [
-       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td><code>qiskit-terra</code></td><td>0.22.0.dev0+4749eb5</td></tr><tr><td><code>qiskit-aer</code></td><td>0.11.0</td></tr><tr><td><code>qiskit-nature</code></td><td>0.5.0</td></tr><tr><td><code>qiskit-finance</code></td><td>0.3.4</td></tr><tr><td><code>qiskit-optimization</code></td><td>0.5.0</td></tr><tr><td><code>qiskit-machine-learning</code></td><td>0.5.0</td></tr><tr><th>System information</th></tr><tr><td>Python version</td><td>3.8.13</td></tr><tr><td>Python compiler</td><td>Clang 12.0.0 </td></tr><tr><td>Python build</td><td>default, Mar 28 2022 06:16:26</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>2</td></tr><tr><td>Memory (Gb)</td><td>12.0</td></tr><tr><td colspan='2'>Thu Sep 15 11:58:34 2022 EDT</td></tr></table>"
+       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td><code>qiskit-terra</code></td><td>0.23.0</td></tr><tr><td><code>qiskit-aer</code></td><td>0.11.1</td></tr><tr><td><code>qiskit-optimization</code></td><td>0.5.0</td></tr><tr><td><code>qiskit-machine-learning</code></td><td>0.6.0</td></tr><tr><th>System information</th></tr><tr><td>Python version</td><td>3.9.15</td></tr><tr><td>Python compiler</td><td>Clang 14.0.0 (clang-1400.0.29.102)</td></tr><tr><td>Python build</td><td>main, Oct 11 2022 22:27:25</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Mon Dec 05 22:42:41 2022 JST</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -583,7 +585,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.13"
+   "version": "3.9.7"
   }
  },
  "nbformat": 4,
diff --git a/docs/tutorials/10_warm_start_qaoa.ipynb b/docs/tutorials/10_warm_start_qaoa.ipynb
index c54118091..d237caaa4 100644
--- a/docs/tutorials/10_warm_start_qaoa.ipynb
+++ b/docs/tutorials/10_warm_start_qaoa.ipynb
@@ -44,9 +44,9 @@
     "from docplex.mp.model import Model\n",
     "\n",
     "# Qiskit imports\n",
-    "from qiskit import BasicAer\n",
-    "from qiskit.utils import QuantumInstance\n",
-    "from qiskit.algorithms import QAOA, NumPyMinimumEigensolver\n",
+    "from qiskit.algorithms.minimum_eigensolvers import QAOA, NumPyMinimumEigensolver\n",
+    "from qiskit.algorithms.optimizers import COBYLA\n",
+    "from qiskit.primitives import Sampler\n",
     "from qiskit.utils.algorithm_globals import algorithm_globals\n",
     "from qiskit_optimization.algorithms import MinimumEigenOptimizer, CplexOptimizer\n",
     "from qiskit_optimization import QuadraticProgram\n",
@@ -268,8 +268,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "objective function value: -17.012055025682855\n",
-      "variable values: x0=0.1752499576180142, x1=1.4803888163988428e-07, x2=0.9709053264087596, x3=0.7384168677494174, x4=0.9999999916475085, x5=0.14438904470168756\n",
+      "objective function value: -17.012055025682685\n",
+      "variable values: x0=0.17524995761801482, x1=1.480388816398847e-07, x2=0.9709053264087595, x3=0.7384168677494172, x4=0.9999999916475085, x5=0.14438904470168706\n",
       "status: SUCCESS\n"
      ]
     }
@@ -289,7 +289,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[0.1752499576180142, 1.4803888163988428e-07, 0.9709053264087596, 0.7384168677494174, 0.9999999916475085, 0.14438904470168756]\n"
+      "[0.17524995761801482, 1.480388816398847e-07, 0.9709053264087595, 0.7384168677494172, 0.9999999916475085, 0.14438904470168706]\n"
      ]
     }
    ],
@@ -320,12 +320,7 @@
    "outputs": [],
    "source": [
     "algorithm_globals.random_seed = 12345\n",
-    "quantum_instance = QuantumInstance(\n",
-    "    BasicAer.get_backend(\"statevector_simulator\"),\n",
-    "    seed_simulator=algorithm_globals.random_seed,\n",
-    "    seed_transpiler=algorithm_globals.random_seed,\n",
-    ")\n",
-    "qaoa_mes = QAOA(quantum_instance=quantum_instance, initial_point=[0.0, 1.0])\n",
+    "qaoa_mes = QAOA(sampler=Sampler(), optimizer=COBYLA(), initial_point=[0.0, 1.0])\n",
     "exact_mes = NumPyMinimumEigensolver()"
    ]
   },
@@ -385,9 +380,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 146.797x385.28 with 1 Axes>"
+       "<Figure size 203.885x535.111 with 1 Axes>"
       ]
      },
      "execution_count": 12,
@@ -429,13 +424,17 @@
    "cell_type": "code",
    "execution_count": 13,
    "id": "pacific-destiny",
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "nbsphinx-thumbnail"
+    ]
+   },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 327.397x385.28 with 1 Axes>"
+       "<Figure size 454.719x535.111 with 1 Axes>"
       ]
      },
      "execution_count": 13,
@@ -473,7 +472,8 @@
    "outputs": [],
    "source": [
     "ws_qaoa_mes = QAOA(\n",
-    "    quantum_instance=quantum_instance,\n",
+    "    sampler=Sampler(),\n",
+    "    optimizer=COBYLA(),\n",
     "    initial_state=init_qc,\n",
     "    mixer=ws_mixer,\n",
     "    initial_point=[0.0, 1.0],\n",
@@ -530,16 +530,16 @@
     {
      "data": {
       "text/plain": [
-       "['001110: value: 16.769, probability: 0.9%',\n",
-       " '011010: value: 15.744, probability: 0.1%',\n",
-       " '001011: value: 14.671, probability: 4.1%',\n",
-       " '101010: value: 14.626, probability: 2.5%',\n",
-       " '010110: value: 14.234, probability: 0.2%',\n",
-       " '100110: value: 13.953, probability: 0.2%',\n",
-       " '000111: value: 13.349, probability: 0.6%',\n",
-       " '110010: value: 12.410, probability: 1.7%',\n",
+       "['001110: value: 16.769, probability: 3.5%',\n",
+       " '011010: value: 15.744, probability: 0.3%',\n",
+       " '001011: value: 14.671, probability: 0.4%',\n",
+       " '101010: value: 14.626, probability: 0.6%',\n",
+       " '010110: value: 14.234, probability: 0.3%',\n",
+       " '100110: value: 13.953, probability: 2.0%',\n",
+       " '000111: value: 13.349, probability: 0.4%',\n",
+       " '110010: value: 12.410, probability: 0.1%',\n",
        " '010011: value: 12.013, probability: 0.0%',\n",
-       " '100011: value: 11.559, probability: 5.5%']"
+       " '100011: value: 11.559, probability: 1.3%']"
       ]
      },
      "execution_count": 17,
@@ -571,12 +571,12 @@
     {
      "data": {
       "text/plain": [
-       "['001110: value: 16.769, probability: 48.4%',\n",
-       " '001011: value: 14.671, probability: 3.4%',\n",
-       " '101010: value: 14.626, probability: 4.5%',\n",
-       " '100110: value: 13.953, probability: 0.6%',\n",
-       " '000111: value: 13.349, probability: 0.4%',\n",
-       " '100011: value: 11.559, probability: 0.1%']"
+       "['001110: value: 16.769, probability: 79.8%',\n",
+       " '001011: value: 14.671, probability: 0.8%',\n",
+       " '101010: value: 14.626, probability: 1.2%',\n",
+       " '100110: value: 13.953, probability: 0.0%',\n",
+       " '000111: value: 13.349, probability: 0.2%',\n",
+       " '100011: value: 11.559, probability: 0.0%']"
       ]
      },
      "execution_count": 18,
@@ -615,7 +615,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "qaoa_mes = QAOA(quantum_instance=quantum_instance, initial_point=[0.0, 1.0])\n",
+    "qaoa_mes = QAOA(sampler=Sampler(), optimizer=COBYLA(), initial_point=[0.0, 1.0])\n",
     "ws_qaoa = WarmStartQAOAOptimizer(\n",
     "    pre_solver=CplexOptimizer(), relax_for_pre_solver=True, qaoa=qaoa_mes, epsilon=0.0\n",
     ")"
@@ -651,12 +651,12 @@
     {
      "data": {
       "text/plain": [
-       "['001110: value: 16.769, probability: 48.4%',\n",
-       " '001011: value: 14.671, probability: 3.4%',\n",
-       " '101010: value: 14.626, probability: 4.5%',\n",
-       " '100110: value: 13.953, probability: 0.6%',\n",
-       " '000111: value: 13.349, probability: 0.4%',\n",
-       " '100011: value: 11.559, probability: 0.1%']"
+       "['001110: value: 16.769, probability: 79.8%',\n",
+       " '001011: value: 14.671, probability: 0.8%',\n",
+       " '101010: value: 14.626, probability: 1.2%',\n",
+       " '100110: value: 13.953, probability: 0.0%',\n",
+       " '000111: value: 13.349, probability: 0.2%',\n",
+       " '100011: value: 11.559, probability: 0.0%']"
       ]
      },
      "execution_count": 22,
@@ -677,7 +677,7 @@
     {
      "data": {
       "text/html": [
-       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td><code>qiskit-terra</code></td><td>0.21.0.dev0+dbd3961</td></tr><tr><td><code>qiskit-aer</code></td><td>0.10.4</td></tr><tr><td><code>qiskit-ibmq-provider</code></td><td>0.19.1</td></tr><tr><td><code>qiskit-optimization</code></td><td>0.4.0</td></tr><tr><th>System information</th></tr><tr><td>Python version</td><td>3.10.4</td></tr><tr><td>Python compiler</td><td>GCC 11.2.0</td></tr><tr><td>Python build</td><td>main, Apr  2 2022 09:04:19</td></tr><tr><td>OS</td><td>Linux</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>14.577545166015625</td></tr><tr><td colspan='2'>Wed May 18 16:05:39 2022 JST</td></tr></table>"
+       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td><code>qiskit-terra</code></td><td>0.23.0</td></tr><tr><td><code>qiskit-aer</code></td><td>0.11.1</td></tr><tr><td><code>qiskit-optimization</code></td><td>0.5.0</td></tr><tr><td><code>qiskit-machine-learning</code></td><td>0.6.0</td></tr><tr><th>System information</th></tr><tr><td>Python version</td><td>3.9.15</td></tr><tr><td>Python compiler</td><td>Clang 14.0.0 (clang-1400.0.29.102)</td></tr><tr><td>Python build</td><td>main, Oct 11 2022 22:27:25</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Mon Dec 05 22:43:10 2022 JST</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -716,8 +716,22 @@
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python"
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
   }
  },
  "nbformat": 4,
diff --git a/docs/tutorials/11_using_classical_optimization_solvers_and_models.ipynb b/docs/tutorials/11_using_classical_optimization_solvers_and_models.ipynb
index 8488acf2f..086b5a676 100644
--- a/docs/tutorials/11_using_classical_optimization_solvers_and_models.ipynb
+++ b/docs/tutorials/11_using_classical_optimization_solvers_and_models.ipynb
@@ -83,7 +83,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Restricted license - for non-production use only - expires 2023-10-25\n",
+      "Restricted license - for non-production use only - expires 2024-10-28\n",
       "cplex\n",
       "objective function value: 4.0\n",
       "variable values: x=1.0, y=4.0\n",
@@ -126,19 +126,19 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Version identifier: 22.1.0.0 | 2022-03-27 | 54982fbec\n",
+      "Version identifier: 22.1.0.0 | 2022-03-09 | 1a383f8ce\n",
       "CPXPARAM_Read_DataCheck                          1\n",
       "CPXPARAM_Threads                                 1\n",
       "CPXPARAM_TimeLimit                               0.10000000000000001\n",
       "Found incumbent of value 0.000000 after 0.00 sec. (0.00 ticks)\n",
-      "Found incumbent of value 4.000000 after 0.01 sec. (0.00 ticks)\n",
+      "Found incumbent of value 4.000000 after 0.00 sec. (0.00 ticks)\n",
       "\n",
       "Root node processing (before b&c):\n",
-      "  Real time             =    0.01 sec. (0.00 ticks)\n",
+      "  Real time             =    0.00 sec. (0.00 ticks)\n",
       "Sequential b&c:\n",
       "  Real time             =    0.00 sec. (0.00 ticks)\n",
       "                          ------------\n",
-      "Total (root+branch&cut) =    0.01 sec. (0.00 ticks)\n",
+      "Total (root+branch&cut) =    0.00 sec. (0.00 ticks)\n",
       "objective function value: 4.0\n",
       "variable values: x=1.0, y=4.0\n",
       "status: SUCCESS\n"
@@ -171,11 +171,11 @@
       "status: SUCCESS\n",
       "\n",
       "display the best 5 solution samples\n",
-      "SolutionSample(x=array([1., 4.]), fval=4.0, probability=0.019, status=<OptimizationResultStatus.SUCCESS: 0>)\n",
-      "SolutionSample(x=array([1., 3.]), fval=3.0, probability=0.025, status=<OptimizationResultStatus.SUCCESS: 0>)\n",
-      "SolutionSample(x=array([1., 2.]), fval=2.0, probability=0.06999999999999999, status=<OptimizationResultStatus.SUCCESS: 0>)\n",
-      "SolutionSample(x=array([1., 1.]), fval=1.0, probability=0.061000000000000006, status=<OptimizationResultStatus.SUCCESS: 0>)\n",
-      "SolutionSample(x=array([0., 0.]), fval=0.0, probability=0.116, status=<OptimizationResultStatus.SUCCESS: 0>)\n"
+      "SolutionSample(x=array([1., 4.]), fval=4.0, probability=0.10186870867618981, status=<OptimizationResultStatus.SUCCESS: 0>)\n",
+      "SolutionSample(x=array([1., 3.]), fval=3.0, probability=0.0983417707484697, status=<OptimizationResultStatus.SUCCESS: 0>)\n",
+      "SolutionSample(x=array([1., 2.]), fval=2.0, probability=0.10501642399046901, status=<OptimizationResultStatus.SUCCESS: 0>)\n",
+      "SolutionSample(x=array([1., 1.]), fval=1.0, probability=0.15585859579394384, status=<OptimizationResultStatus.SUCCESS: 0>)\n",
+      "SolutionSample(x=array([0., 0.]), fval=0.0, probability=0.06365119827028128, status=<OptimizationResultStatus.SUCCESS: 0>)\n"
      ]
     }
    ],
@@ -183,12 +183,11 @@
     "from qiskit_optimization.algorithms import MinimumEigenOptimizer\n",
     "\n",
     "from qiskit_aer import Aer\n",
-    "from qiskit.utils import QuantumInstance\n",
-    "from qiskit.algorithms import QAOA\n",
+    "from qiskit.algorithms.minimum_eigensolvers import QAOA\n",
     "from qiskit.algorithms.optimizers import COBYLA\n",
+    "from qiskit.primitives import Sampler\n",
     "\n",
-    "qins = QuantumInstance(backend=Aer.get_backend(\"aer_simulator\"), shots=1000)\n",
-    "meo = MinimumEigenOptimizer(QAOA(COBYLA(maxiter=100), quantum_instance=qins))\n",
+    "meo = MinimumEigenOptimizer(QAOA(sampler=Sampler(), optimizer=COBYLA(maxiter=100)))\n",
     "result = meo.solve(qp)\n",
     "print(result.prettyprint())\n",
     "print(\"\\ndisplay the best 5 solution samples\")\n",
@@ -254,9 +253,9 @@
      "output_type": "stream",
      "text": [
       "Maximize\n",
-      "  <gurobi.QuadExpr: 0.0 + [ x * y ]>\n",
+      "  0.0 + [ x * y ]\n",
       "Subject To\n",
-      "  R0: <gurobi.LinExpr: x + -1.0 y> <= 0\n",
+      "  R0: x + -1.0 y <= 0\n",
       "Bounds\n",
       "  -1 <= y <= 4\n",
       "Binaries\n",
@@ -361,9 +360,9 @@
      "text": [
       "convert docplex to gurobipy via QuadraticProgram\n",
       "Maximize\n",
-      "  <gurobi.QuadExpr: 0.0 + [ x * y ]>\n",
+      "  0.0 + [ x * y ]\n",
       "Subject To\n",
-      "  c0: <gurobi.LinExpr: x + -1.0 y> <= 0\n",
+      "  c0: x + -1.0 y <= 0\n",
       "Bounds\n",
       "  -1 <= y <= 4\n",
       "Binaries\n",
@@ -488,6 +487,7 @@
       "CPLEX\n",
       "solution for: docplex\n",
       "objective: 6\n",
+      "status: OPTIMAL_SOLUTION(2)\n",
       "x=1\n",
       "y=2\n",
       "z=-1\n",
@@ -512,7 +512,7 @@
     {
      "data": {
       "text/html": [
-       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td><code>qiskit-terra</code></td><td>0.22.0.dev0+4749eb5</td></tr><tr><td><code>qiskit-aer</code></td><td>0.11.0</td></tr><tr><td><code>qiskit-nature</code></td><td>0.5.0</td></tr><tr><td><code>qiskit-finance</code></td><td>0.3.4</td></tr><tr><td><code>qiskit-optimization</code></td><td>0.5.0</td></tr><tr><td><code>qiskit-machine-learning</code></td><td>0.5.0</td></tr><tr><th>System information</th></tr><tr><td>Python version</td><td>3.8.13</td></tr><tr><td>Python compiler</td><td>Clang 12.0.0 </td></tr><tr><td>Python build</td><td>default, Mar 28 2022 06:16:26</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>2</td></tr><tr><td>Memory (Gb)</td><td>12.0</td></tr><tr><td colspan='2'>Thu Sep 15 12:02:06 2022 EDT</td></tr></table>"
+       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td><code>qiskit-terra</code></td><td>0.23.0</td></tr><tr><td><code>qiskit-aer</code></td><td>0.11.1</td></tr><tr><td><code>qiskit-optimization</code></td><td>0.5.0</td></tr><tr><td><code>qiskit-machine-learning</code></td><td>0.6.0</td></tr><tr><th>System information</th></tr><tr><td>Python version</td><td>3.9.15</td></tr><tr><td>Python compiler</td><td>Clang 14.0.0 (clang-1400.0.29.102)</td></tr><tr><td>Python build</td><td>main, Oct 11 2022 22:27:25</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Mon Dec 05 22:42:49 2022 JST</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -543,22 +543,8 @@
   }
  ],
  "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
   "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.13"
+   "name": "python"
   }
  },
  "nbformat": 4,
diff --git a/docs/tutorials/12_qaoa_runtime.ipynb b/docs/tutorials/12_qaoa_runtime.ipynb
index 0abf1d934..678ee69c4 100644
--- a/docs/tutorials/12_qaoa_runtime.ipynb
+++ b/docs/tutorials/12_qaoa_runtime.ipynb
@@ -5,10 +5,24 @@
    "id": "d9a5a73c",
    "metadata": {},
    "source": [
-    "# QAOA Runtime\n",
-    "\n",
-    "The Qiskit runtime is an execution model that permits us to run an entire program on the backend side. Here, we discuss how to run the QAOA algorithm in the Qiskit runtime. We will discuss several of the features that this first version of the QAOA Runtime makes available.\n",
-    "\n"
+    "# QAOA Runtime"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d492e9c3",
+   "metadata": {},
+   "source": [
+    ".. warning::\n",
+    "  The `VQEClient` and `QAOAClient` discussed in this tutorial are **DEPRECATED** as of version 0.6 of Qiskit Optimization and will be removed no sooner than 3 months after the release. Instead you should use the new primitives based `SamplingVQE` and `QAOA` algorithms respectively with the Qiskit IBM Runtime Sampler primitive. For more details on how to migrate check out these guides <https://qisk.it/algo_migration#vqe> and <https://qisk.it/algo_migration#qaoa>."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cb8d128b",
+   "metadata": {},
+   "source": [
+    "The Qiskit runtime is an execution model that permits us to run an entire program on the backend side. Here, we discuss how to run the QAOA algorithm in the Qiskit runtime. We will discuss several of the features that this first version of the QAOA Runtime makes available."
    ]
   },
   {
@@ -322,7 +336,11 @@
    "cell_type": "code",
    "execution_count": 10,
    "id": "73a06065",
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "nbsphinx-thumbnail"
+    ]
+   },
    "outputs": [
     {
      "data": {
@@ -638,8 +656,22 @@
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python"
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.6"
   },
   "nbsphinx": {
    "execute": "never"
diff --git a/qiskit_optimization/VERSION.txt b/qiskit_optimization/VERSION.txt
index 8f0916f76..a918a2aa1 100644
--- a/qiskit_optimization/VERSION.txt
+++ b/qiskit_optimization/VERSION.txt
@@ -1 +1 @@
-0.5.0
+0.6.0
diff --git a/qiskit_optimization/__init__.py b/qiskit_optimization/__init__.py
index 5753d7c90..23c9443cd 100644
--- a/qiskit_optimization/__init__.py
+++ b/qiskit_optimization/__init__.py
@@ -26,10 +26,11 @@
 A uniform interface as well as automatic conversion between different problem representations
 allows users to solve problems using a large set of algorithms, from variational quantum algorithms,
 such as the Quantum Approximate Optimization Algorithm
-(:class:`~qiskit.algorithms.QAOA`), to
+(:class:`~qiskit.algorithms.minimum_eigensolver.QAOA`), to
 `Grover Adaptive Search <https://arxiv.org/abs/quant-ph/9607014>`_
 (:class:`~algorithms.GroverOptimizer`), leveraging
-fundamental :mod:`~qiskit.algorithms` provided by Qiskit Terra. Furthermore, the modular design
+fundamental :mod:`~qiskit.algorithms.minimum_eigensolver` provided by Qiskit Terra.
+Furthermore, the modular design
 of the optimization module allows it to be easily extended and facilitates rapid development and
 testing of new algorithms. Compatible classical optimizers are also provided for testing,
 validation, and benchmarking.
diff --git a/qiskit_optimization/algorithms/__init__.py b/qiskit_optimization/algorithms/__init__.py
index 7fbacb401..c4ab6b0e8 100644
--- a/qiskit_optimization/algorithms/__init__.py
+++ b/qiskit_optimization/algorithms/__init__.py
@@ -1,6 +1,6 @@
 # This code is part of Qiskit.
 #
-# (C) Copyright IBM 2019, 2022.
+# (C) Copyright IBM 2019, 2023.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -105,6 +105,9 @@
 
 __all__ = [
     "ADMMOptimizer",
+    "ADMMOptimizationResult",
+    "ADMMState",
+    "ADMMParameters",
     "OptimizationAlgorithm",
     "OptimizationResult",
     "OptimizationResultStatus",
@@ -119,6 +122,7 @@
     "MeanAggregator",
     "MinimumEigenOptimizer",
     "MinimumEigenOptimizationResult",
+    "MultiStartOptimizer",
     "RecursiveMinimumEigenOptimizer",
     "RecursiveMinimumEigenOptimizationResult",
     "IntermediateResult",
diff --git a/qiskit_optimization/algorithms/admm_optimizer.py b/qiskit_optimization/algorithms/admm_optimizer.py
index 40c3911e7..881e05be7 100644
--- a/qiskit_optimization/algorithms/admm_optimizer.py
+++ b/qiskit_optimization/algorithms/admm_optimizer.py
@@ -17,21 +17,21 @@
 from typing import List, Optional, Tuple, cast
 
 import numpy as np
-from qiskit.algorithms import NumPyMinimumEigensolver
+from qiskit.algorithms.minimum_eigensolvers import NumPyMinimumEigensolver
 
+from ..converters import MaximizeToMinimize
+from ..problems.constraint import Constraint
+from ..problems.linear_constraint import LinearConstraint
+from ..problems.linear_expression import LinearExpression
+from ..problems.quadratic_program import QuadraticProgram
+from ..problems.variable import Variable, VarType
 from .minimum_eigen_optimizer import MinimumEigenOptimizer
 from .optimization_algorithm import (
-    OptimizationResultStatus,
     OptimizationAlgorithm,
     OptimizationResult,
+    OptimizationResultStatus,
 )
 from .slsqp_optimizer import SlsqpOptimizer
-from ..problems.constraint import Constraint
-from ..problems.linear_constraint import LinearConstraint
-from ..problems.linear_expression import LinearExpression
-from ..problems.quadratic_program import QuadraticProgram
-from ..problems.variable import VarType, Variable
-from ..converters import MaximizeToMinimize
 
 UPDATE_RHO_BY_TEN_PERCENT = 0
 UPDATE_RHO_BY_RESIDUALS = 1
diff --git a/qiskit_optimization/algorithms/grover_optimizer.py b/qiskit_optimization/algorithms/grover_optimizer.py
index d6a75cde0..f77da7e0b 100644
--- a/qiskit_optimization/algorithms/grover_optimizer.py
+++ b/qiskit_optimization/algorithms/grover_optimizer.py
@@ -14,30 +14,30 @@
 
 import logging
 import math
+import warnings
 from copy import deepcopy
-from typing import Optional, Dict, Union, List, cast
+from typing import Dict, List, Optional, Union, cast
 
 import numpy as np
-
 from qiskit import QuantumCircuit, QuantumRegister
 from qiskit.algorithms import AmplificationProblem
-from qiskit.utils import QuantumInstance, algorithm_globals
 from qiskit.algorithms.amplitude_amplifiers.grover import Grover
 from qiskit.circuit.library import QuadraticForm
+from qiskit.primitives import BaseSampler
 from qiskit.providers import Backend
 from qiskit.quantum_info import partial_trace
+from qiskit.utils import QuantumInstance, algorithm_globals
+
+from ..converters.quadratic_program_to_qubo import QuadraticProgramConverter, QuadraticProgramToQubo
+from ..exceptions import QiskitOptimizationError
+from ..problems import Variable
+from ..problems.quadratic_program import QuadraticProgram
 from .optimization_algorithm import (
-    OptimizationResultStatus,
     OptimizationAlgorithm,
     OptimizationResult,
+    OptimizationResultStatus,
     SolutionSample,
 )
-from ..converters.quadratic_program_to_qubo import (
-    QuadraticProgramToQubo,
-    QuadraticProgramConverter,
-)
-from ..problems import Variable
-from ..problems.quadratic_program import QuadraticProgram
 
 logger = logging.getLogger(__name__)
 
@@ -54,6 +54,7 @@ def __init__(
             Union[QuadraticProgramConverter, List[QuadraticProgramConverter]]
         ] = None,
         penalty: Optional[float] = None,
+        sampler: Optional[BaseSampler] = None,
     ) -> None:
         """
         Args:
@@ -66,20 +67,35 @@ def __init__(
                 :class:`~qiskit_optimization.converters.QuadraticProgramToQubo` will be used.
             penalty: The penalty factor used in the default
                 :class:`~qiskit_optimization.converters.QuadraticProgramToQubo` converter
+            sampler: A Sampler to use for sampling the results of the circuits.
 
         Raises:
+            ValueError: If both a quantum instance and sampler are set.
             TypeError: When there one of converters is an invalid type.
         """
         self._num_value_qubits = num_value_qubits
         self._num_key_qubits = 0
         self._n_iterations = num_iterations
-        self._quantum_instance = None  # type: Optional[QuantumInstance]
         self._circuit_results = {}  # type: dict
+        self._converters = self._prepare_converters(converters, penalty)
 
-        if quantum_instance is not None:
-            self.quantum_instance = quantum_instance
+        if quantum_instance is not None and sampler is not None:
+            raise ValueError("Only one of quantum_instance or sampler can be passed, not both!")
 
-        self._converters = self._prepare_converters(converters, penalty)
+        self._quantum_instance = None  # type: Optional[QuantumInstance]
+        if quantum_instance is not None:
+            warnings.warn(
+                "The quantum_instance argument has been superseded by the sampler argument. "
+                "This argument will be deprecated in a future release and subsequently "
+                "removed after that.",
+                category=PendingDeprecationWarning,
+                stacklevel=2,
+            )
+            with warnings.catch_warnings():
+                warnings.simplefilter("ignore", category=PendingDeprecationWarning)
+                self.quantum_instance = quantum_instance
+
+        self._sampler = sampler
 
     @property
     def quantum_instance(self) -> QuantumInstance:
@@ -88,6 +104,13 @@ def quantum_instance(self) -> QuantumInstance:
         Returns:
             The quantum instance used in the algorithm.
         """
+        warnings.warn(
+            "The quantum_instance argument has been superseded by the sampler argument. "
+            "This argument will be deprecated in a future release and subsequently "
+            "removed after that.",
+            category=PendingDeprecationWarning,
+            stacklevel=2,
+        )
         return self._quantum_instance
 
     @quantum_instance.setter
@@ -97,6 +120,13 @@ def quantum_instance(self, quantum_instance: Union[Backend, QuantumInstance]) ->
         Args:
             quantum_instance: The quantum instance to be used in the algorithm.
         """
+        warnings.warn(
+            "The GroverOptimizer.quantum_instance setter is pending deprecation. "
+            "This property will be deprecated in a future release and subsequently "
+            "removed after that.",
+            category=PendingDeprecationWarning,
+            stacklevel=2,
+        )
         if isinstance(quantum_instance, Backend):
             self._quantum_instance = QuantumInstance(quantum_instance)
         else:
@@ -162,11 +192,16 @@ def solve(self, problem: QuadraticProgram) -> OptimizationResult:
             The result of the optimizer applied to the problem.
 
         Raises:
+            ValueError: If a quantum instance or a sampler has not been provided.
+            ValueError: If both a quantum instance and sampler are set.
             AttributeError: If the quantum instance has not been set.
             QiskitOptimizationError: If the problem is incompatible with the optimizer.
         """
-        if self.quantum_instance is None:
-            raise AttributeError("The quantum instance or backend has not been set.")
+        if self._sampler is None and self._quantum_instance is None:
+            raise ValueError("A quantum instance or sampler must be provided.")
+
+        if self._quantum_instance is not None and self._sampler is not None:
+            raise ValueError("Only one of quantum_instance or sampler can be passed, not both!")
 
         self._verify_compatibility(problem)
 
@@ -199,7 +234,7 @@ def solve(self, problem: QuadraticProgram) -> OptimizationResult:
         # Initialize oracle helper object.
         qr_key_value = QuantumRegister(self._num_key_qubits + self._num_value_qubits)
         orig_constant = problem_.objective.constant
-        measurement = not self.quantum_instance.is_statevector
+        measurement = self._quantum_instance is None or not self._quantum_instance.is_statevector
         oracle, is_good_state = self._get_oracle(qr_key_value)
 
         while not optimum_found:
@@ -246,15 +281,19 @@ def solve(self, problem: QuadraticProgram) -> OptimizationResult:
                     threshold = optimum_value
 
                     # trace out work qubits and store samples
-                    if self._quantum_instance.is_statevector:
-                        indices = list(range(n_key, len(outcome)))
-                        rho = partial_trace(self._circuit_results, indices)
-                        self._circuit_results = cast(Dict, np.diag(rho.data) ** 0.5)
-                    else:
+                    if self._sampler is not None:
                         self._circuit_results = {
                             i[-1 * n_key :]: v for i, v in self._circuit_results.items()
                         }
-
+                    else:
+                        if self._quantum_instance.is_statevector:
+                            indices = list(range(n_key, len(outcome)))
+                            rho = partial_trace(self._circuit_results, indices)
+                            self._circuit_results = cast(Dict, np.diag(rho.data) ** 0.5)
+                        else:
+                            self._circuit_results = {
+                                i[-1 * n_key :]: v for i, v in self._circuit_results.items()
+                            }
                     raw_samples = self._eigenvector_to_solutions(
                         self._circuit_results, problem_init
                     )
@@ -312,33 +351,52 @@ def solve(self, problem: QuadraticProgram) -> OptimizationResult:
 
     def _measure(self, circuit: QuantumCircuit) -> str:
         """Get probabilities from the given backend, and picks a random outcome."""
-        probs = self._get_probs(circuit)
+        probs = self._get_prob_dist(circuit)
         logger.info("Frequencies: %s", probs)
         # Pick a random outcome.
         return algorithm_globals.random.choice(list(probs.keys()), 1, p=list(probs.values()))[0]
 
-    def _get_probs(self, qc: QuantumCircuit) -> Dict[str, float]:
+    def _get_prob_dist(self, qc: QuantumCircuit) -> Dict[str, float]:
         """Gets probabilities from a given backend."""
         # Execute job and filter results.
-        result = self.quantum_instance.execute(qc)
-        if self.quantum_instance.is_statevector:
-            state = result.get_statevector(qc)
-            if not isinstance(state, np.ndarray):
-                state = state.data
-            keys = [
-                bin(i)[2::].rjust(int(np.log2(len(state))), "0")[::-1] for i in range(0, len(state))
-            ]
-            probs = [abs(a) ** 2 for a in state]
-            total = math.fsum(probs)
-            probs = [p / total for p in probs]
-            hist = {key: prob for key, prob in zip(keys, probs) if prob > 0}
-            self._circuit_results = state
+        if self._sampler is not None:
+            job = self._sampler.run([qc])
+
+            try:
+                result = job.result()
+            except Exception as exc:
+                raise QiskitOptimizationError("Sampler job failed.") from exc
+            quasi_dist = result.quasi_dists[0]
+            raw_prob_dist = {
+                k: v
+                for k, v in quasi_dist.binary_probabilities(qc.num_qubits).items()
+                if v >= self._MIN_PROBABILITY
+            }
+            prob_dist = {k[::-1]: v for k, v in raw_prob_dist.items()}
+            self._circuit_results = {i: v**0.5 for i, v in raw_prob_dist.items()}
         else:
-            state = result.get_counts(qc)
-            shots = self.quantum_instance.run_config.shots
-            hist = {key[::-1]: val / shots for key, val in sorted(state.items()) if val > 0}
-            self._circuit_results = {b: (v / shots) ** 0.5 for (b, v) in state.items()}
-        return hist
+            result = self._quantum_instance.execute(qc)
+            if self._quantum_instance.is_statevector:
+                state = result.get_statevector(qc)
+                if not isinstance(state, np.ndarray):
+                    state = state.data
+                keys = [
+                    bin(i)[2::].rjust(int(np.log2(len(state))), "0")[::-1]
+                    for i in range(0, len(state))
+                ]
+                probs = [abs(a) ** 2 for a in state]
+                total = math.fsum(probs)
+                probs = [p / total for p in probs]
+                prob_dist = {key: prob for key, prob in zip(keys, probs) if prob > 0}
+                self._circuit_results = state
+            else:
+                state = result.get_counts(qc)
+                shots = self._quantum_instance.run_config.shots
+                prob_dist = {
+                    key[::-1]: val / shots for key, val in sorted(state.items()) if val > 0
+                }
+                self._circuit_results = {b: (v / shots) ** 0.5 for (b, v) in state.items()}
+        return prob_dist
 
     @staticmethod
     def _bin_to_int(v: str, num_value_bits: int) -> int:
diff --git a/qiskit_optimization/algorithms/minimum_eigen_optimizer.py b/qiskit_optimization/algorithms/minimum_eigen_optimizer.py
index 0a86bba28..d1b952631 100644
--- a/qiskit_optimization/algorithms/minimum_eigen_optimizer.py
+++ b/qiskit_optimization/algorithms/minimum_eigen_optimizer.py
@@ -1,6 +1,6 @@
 # This code is part of Qiskit.
 #
-# (C) Copyright IBM 2020, 2021.
+# (C) Copyright IBM 2020, 2023.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -11,24 +11,38 @@
 # that they have been altered from the originals.
 
 """A wrapper for minimum eigen solvers to be used within the optimization module."""
-from typing import Optional, Union, List, cast
+from typing import List, Optional, Union, cast
 
 import numpy as np
+from qiskit.algorithms.minimum_eigen_solvers import MinimumEigensolver as LegacyMinimumEigensolver
+from qiskit.algorithms.minimum_eigen_solvers import (
+    MinimumEigensolverResult as LegacyMinimumEigensolverResult,
+)
+from qiskit.algorithms.minimum_eigensolvers import (
+    NumPyMinimumEigensolver,
+    NumPyMinimumEigensolverResult,
+    SamplingMinimumEigensolver,
+    SamplingMinimumEigensolverResult,
+    VQE,
+)
+from qiskit.opflow import OperatorBase, PauliOp, PauliSumOp
 
-from qiskit.algorithms import MinimumEigensolver, MinimumEigensolverResult
-from qiskit.opflow import OperatorBase
+from ..converters.quadratic_program_to_qubo import QuadraticProgramConverter, QuadraticProgramToQubo
+from ..exceptions import QiskitOptimizationError
+from ..problems.quadratic_program import QuadraticProgram, Variable
 from .optimization_algorithm import (
-    OptimizationResultStatus,
     OptimizationAlgorithm,
     OptimizationResult,
+    OptimizationResultStatus,
     SolutionSample,
 )
-from ..exceptions import QiskitOptimizationError
-from ..converters.quadratic_program_to_qubo import (
-    QuadraticProgramToQubo,
-    QuadraticProgramConverter,
-)
-from ..problems.quadratic_program import QuadraticProgram, Variable
+
+MinimumEigensolver = Union[
+    SamplingMinimumEigensolver, NumPyMinimumEigensolver, LegacyMinimumEigensolver
+]
+MinimumEigensolverResult = Union[
+    SamplingMinimumEigensolverResult, NumPyMinimumEigensolverResult, LegacyMinimumEigensolverResult
+]
 
 
 class MinimumEigenOptimizationResult(OptimizationResult):
@@ -101,7 +115,7 @@ class MinimumEigenOptimizer(OptimizationAlgorithm):
 
     .. code-block::
 
-        from qiskit.algorithms import QAOA
+        from qiskit.algorithms.minimum_eigensolver import QAOA
         from qiskit_optimization.problems import QuadraticProgram
         from qiskit_optimization.algorithms import MinimumEigenOptimizer
         problem = QuadraticProgram()
@@ -134,14 +148,30 @@ def __init__(
                 :class:`~qiskit_optimization.converters.QuadraticProgramToQubo` will be used.
 
         Raises:
+            TypeError: If minimum eigensolver has an invalid type.
             TypeError: When one of converters has an invalid type.
             QiskitOptimizationError: When the minimum eigensolver does not return an eigenstate.
         """
-
+        if isinstance(min_eigen_solver, VQE):
+            raise TypeError(
+                "MinimumEigenOptimizer does not support this VQE. You can use  "
+                "qiskit.algorithms.minimum_eigensolvers.SamplingVQE instead."
+            )
+        if not isinstance(
+            min_eigen_solver,
+            (SamplingMinimumEigensolver, NumPyMinimumEigensolver, LegacyMinimumEigensolver),
+        ):
+            raise TypeError(
+                "MinimumEigenOptimizer supports "
+                "qiskit.algorithms.minimum_eigensolvers.SamplingMinimumEigensolver, "
+                "qiskit.algorithms.minimum_eigensolvers.NumPyMinimumEigensolver, and "
+                "qiskit.algorithms.minimum_eigen_solvers.MinimumEigensolver. "
+                f"But {type(min_eigen_solver)} is given."
+            )
         if not min_eigen_solver.supports_aux_operators():
             raise QiskitOptimizationError(
                 "Given MinimumEigensolver does not return the eigenstate "
-                + "and is not supported by the MinimumEigenOptimizer."
+                "and is not supported by the MinimumEigenOptimizer."
             )
         self._min_eigen_solver = min_eigen_solver
         self._penalty = penalty
@@ -192,7 +222,7 @@ def solve(self, problem: QuadraticProgram) -> MinimumEigenOptimizationResult:
         problem_ = self._convert(problem, self._converters)
 
         # construct operator and offset
-        operator, offset = problem_.to_ising()
+        operator, offset = problem_.to_ising(opflow=True)
 
         return self._solve_internal(operator, offset, problem_, problem)
 
@@ -206,6 +236,9 @@ def _solve_internal(
         # only try to solve non-empty Ising Hamiltonians
         eigen_result: Optional[MinimumEigensolverResult] = None
         if operator.num_qubits > 0:
+            # NumPyEigensolver does not accept PauliOp but PauliSumOp
+            if isinstance(operator, PauliOp):
+                operator = PauliSumOp.from_list([(operator.primitive.to_label(), operator.coeff)])
             # approximate ground state of operator using min eigen solver
             eigen_result = self._min_eigen_solver.compute_minimum_eigenvalue(operator)
             # analyze results
diff --git a/qiskit_optimization/algorithms/optimization_algorithm.py b/qiskit_optimization/algorithms/optimization_algorithm.py
index 050d78ca5..d67c9b73b 100644
--- a/qiskit_optimization/algorithms/optimization_algorithm.py
+++ b/qiskit_optimization/algorithms/optimization_algorithm.py
@@ -15,16 +15,20 @@
 from abc import ABC, abstractmethod
 from dataclasses import dataclass
 from enum import Enum
-from typing import List, Union, Any, Optional, Dict, Type, Tuple, cast
-from warnings import warn
+from logging import getLogger
+from typing import Any, Dict, List, Optional, Tuple, Type, Union, cast
 
 import numpy as np
+from qiskit.opflow import DictStateFn, StateFn
+from qiskit.quantum_info import Statevector
+from qiskit.result import QuasiDistribution
 
-from qiskit.opflow import StateFn, DictStateFn
+from ..converters.quadratic_program_to_qubo import QuadraticProgramConverter, QuadraticProgramToQubo
 from ..exceptions import QiskitOptimizationError
-from ..converters.quadratic_program_to_qubo import QuadraticProgramToQubo, QuadraticProgramConverter
 from ..problems.quadratic_program import QuadraticProgram, Variable
 
+logger = getLogger(__name__)
+
 
 class OptimizationResultStatus(Enum):
     """Termination status of an optimization algorithm."""
@@ -135,7 +139,7 @@ def __init__(
         if samples:
             sum_prob = np.sum([e.probability for e in samples])
             if not np.isclose(sum_prob, 1.0):
-                warn(f"The sum of probability of samples is not close to 1: {sum_prob}")
+                logger.debug("The sum of probability of samples is not close to 1: %f", sum_prob)
             self._samples = samples
         else:
             self._samples = [
@@ -289,6 +293,8 @@ def samples(self) -> List[SolutionSample]:
 class OptimizationAlgorithm(ABC):
     """An abstract class for optimization algorithms in Qiskit's optimization module."""
 
+    _MIN_PROBABILITY = 1e-6
+
     @abstractmethod
     def get_compatibility_msg(self, problem: QuadraticProgram) -> str:
         """Checks whether a given problem can be solved with the optimizer implementing this method.
@@ -518,9 +524,9 @@ def _interpret_samples(
 
     @staticmethod
     def _eigenvector_to_solutions(
-        eigenvector: Union[dict, np.ndarray, StateFn],
+        eigenvector: Union[QuasiDistribution, Statevector, dict, np.ndarray, StateFn],
         qubo: QuadraticProgram,
-        min_probability: float = 1e-6,
+        min_probability: float = _MIN_PROBABILITY,
     ) -> List[SolutionSample]:
         """Convert the eigenvector to the bitstrings and corresponding eigenvalues.
 
@@ -566,7 +572,25 @@ def generate_solution(bitstr, qubo, probability):
             )
 
         solutions = []
-        if isinstance(eigenvector, dict):
+        if isinstance(eigenvector, QuasiDistribution):
+            probabilities = eigenvector.binary_probabilities()
+            # iterate over all samples
+            for bitstr, sampling_probability in probabilities.items():
+                # add the bitstring, if the sampling probability exceeds the threshold
+                if sampling_probability >= min_probability:
+                    solutions.append(generate_solution(bitstr, qubo, sampling_probability))
+
+        elif isinstance(eigenvector, Statevector):
+            probabilities = eigenvector.probabilities()
+            num_qubits = eigenvector.num_qubits
+            # iterate over all states and their sampling probabilities
+            for i, sampling_probability in enumerate(probabilities):
+                # add the i-th state if the sampling probability exceeds the threshold
+                if sampling_probability >= min_probability:
+                    bitstr = f"{i:b}".rjust(num_qubits, "0")
+                    solutions.append(generate_solution(bitstr, qubo, sampling_probability))
+
+        elif isinstance(eigenvector, dict):
             # When eigenvector is a dict, square the values since the values are normalized.
             # See https://github.com/Qiskit/qiskit-terra/pull/5496 for more details.
             probabilities = {bitstr: val**2 for (bitstr, val) in eigenvector.items()}
@@ -579,7 +603,6 @@ def generate_solution(bitstr, qubo, probability):
         elif isinstance(eigenvector, np.ndarray):
             num_qubits = int(np.log2(eigenvector.size))
             probabilities = np.abs(eigenvector * eigenvector.conj())
-
             # iterate over all states and their sampling probabilities
             for i, sampling_probability in enumerate(probabilities):
                 # add the i-th state if the sampling probability exceeds the threshold
@@ -588,6 +611,8 @@ def generate_solution(bitstr, qubo, probability):
                     solutions.append(generate_solution(bitstr, qubo, sampling_probability))
 
         else:
-            raise TypeError("Unsupported format of eigenvector. Provide a dict or numpy.ndarray.")
-
+            raise TypeError(
+                f"Eigenvector should be QuasiDistribution, Statevector, dict or numpy.ndarray. "
+                f"But, it was {type(eigenvector)}."
+            )
         return solutions
diff --git a/qiskit_optimization/algorithms/recursive_minimum_eigen_optimizer.py b/qiskit_optimization/algorithms/recursive_minimum_eigen_optimizer.py
index 26c1184a6..c3149f3fc 100644
--- a/qiskit_optimization/algorithms/recursive_minimum_eigen_optimizer.py
+++ b/qiskit_optimization/algorithms/recursive_minimum_eigen_optimizer.py
@@ -14,28 +14,22 @@
 
 from copy import deepcopy
 from enum import Enum
-from typing import Optional, Union, List, Tuple, Dict, cast
+from typing import Dict, List, Optional, Tuple, Union, cast
 
 import numpy as np
-from qiskit.algorithms import NumPyMinimumEigensolver
+from qiskit.algorithms.minimum_eigensolvers import NumPyMinimumEigensolver
 from qiskit.utils.validation import validate_min
 
-from .minimum_eigen_optimizer import (
-    MinimumEigenOptimizer,
-    MinimumEigenOptimizationResult,
-)
+from ..converters.quadratic_program_to_qubo import QuadraticProgramConverter, QuadraticProgramToQubo
+from ..exceptions import QiskitOptimizationError
+from ..problems import Variable
+from ..problems.quadratic_program import QuadraticProgram
+from .minimum_eigen_optimizer import MinimumEigenOptimizationResult, MinimumEigenOptimizer
 from .optimization_algorithm import (
-    OptimizationResultStatus,
     OptimizationAlgorithm,
     OptimizationResult,
+    OptimizationResultStatus,
 )
-from ..converters.quadratic_program_to_qubo import (
-    QuadraticProgramToQubo,
-    QuadraticProgramConverter,
-)
-from ..exceptions import QiskitOptimizationError
-from ..problems import Variable
-from ..problems.quadratic_program import QuadraticProgram
 
 
 class IntermediateResult(Enum):
@@ -123,7 +117,7 @@ class RecursiveMinimumEigenOptimizer(OptimizationAlgorithm):
 
     .. code-block:: python
 
-        from qiskit.algorithms import QAOA
+        from qiskit.algorithms.minimum_eigensolver import QAOA
         from qiskit_optimization.problems import QuadraticProgram
         from qiskit_optimization.algorithms import (
             MinimumEigenOptimizer, RecursiveMinimumEigenOptimizer
@@ -167,7 +161,7 @@ def __init__(
             min_num_vars_optimizer: This optimizer is used after the recursive scheme for the
                 problem with the remaining variables. Default value is
                 :class:`~qiskit_optimization.algorithms.MinimumEigenOptimizer` created on top of
-                :class:`~qiskit.algorithms.minimum_eigen_solver.NumPyMinimumEigensolver`.
+                :class:`~qiskit.algorithms.minimum_eigensolver.NumPyMinimumEigensolver`.
             penalty: The factor that is used to scale the penalty terms corresponding to linear
                 equality constraints.
             history: Whether the intermediate results are stored.
diff --git a/qiskit_optimization/algorithms/warm_start_qaoa_optimizer.py b/qiskit_optimization/algorithms/warm_start_qaoa_optimizer.py
index 98016cbdd..6c41b9d68 100644
--- a/qiskit_optimization/algorithms/warm_start_qaoa_optimizer.py
+++ b/qiskit_optimization/algorithms/warm_start_qaoa_optimizer.py
@@ -1,6 +1,6 @@
 # This code is part of Qiskit.
 #
-# (C) Copyright IBM 2021.
+# (C) Copyright IBM 2021, 2023.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -14,26 +14,20 @@
 
 import copy
 from abc import ABC, abstractmethod
-from typing import Optional, List, Union, Dict, Tuple, cast
+from typing import Dict, List, Optional, Tuple, Union, cast
 
 import numpy as np
 from qiskit import QuantumCircuit
-from qiskit.algorithms import QAOA
+from qiskit.algorithms import QAOA as LegacyQAOA
+from qiskit.algorithms.minimum_eigensolvers import QAOA
 from qiskit.circuit import Parameter
 
-from .minimum_eigen_optimizer import (
-    MinimumEigenOptimizer,
-    MinimumEigenOptimizationResult,
-)
-from .optimization_algorithm import (
-    OptimizationAlgorithm,
-    OptimizationResultStatus,
-    SolutionSample,
-)
 from ..converters.quadratic_program_converter import QuadraticProgramConverter
 from ..exceptions import QiskitOptimizationError
 from ..problems.quadratic_program import QuadraticProgram
 from ..problems.variable import VarType
+from .minimum_eigen_optimizer import MinimumEigenOptimizationResult, MinimumEigenOptimizer
+from .optimization_algorithm import OptimizationAlgorithm, OptimizationResultStatus, SolutionSample
 
 
 class BaseAggregator(ABC):
@@ -209,7 +203,7 @@ def __init__(
         self,
         pre_solver: OptimizationAlgorithm,
         relax_for_pre_solver: bool,
-        qaoa: QAOA,
+        qaoa: Union[QAOA, LegacyQAOA],
         epsilon: float = 0.25,
         num_initial_solutions: int = 1,
         warm_start_factory: Optional[WarmStartQAOAFactory] = None,
@@ -317,7 +311,7 @@ def solve(self, problem: QuadraticProgram) -> MinimumEigenOptimizationResult:
         pre_solutions = opt_result.samples[:num_pre_solutions]
 
         # construct operator and offset
-        operator, offset = converted_problem.to_ising()
+        operator, offset = converted_problem.to_ising(opflow=True)
 
         results: List[MinimumEigenOptimizationResult] = []
         for pre_solution in pre_solutions:
diff --git a/qiskit_optimization/applications/__init__.py b/qiskit_optimization/applications/__init__.py
index 27fda9223..f5ea862a2 100644
--- a/qiskit_optimization/applications/__init__.py
+++ b/qiskit_optimization/applications/__init__.py
@@ -1,6 +1,6 @@
 # This code is part of Qiskit.
 #
-# (C) Copyright IBM 2020, 2021.
+# (C) Copyright IBM 2020, 2023.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -70,6 +70,7 @@
     "Clique",
     "ExactCover",
     "GraphOptimizationApplication",
+    "GraphPartition",
     "Knapsack",
     "Maxcut",
     "NumberPartition",
diff --git a/qiskit_optimization/applications/optimization_application.py b/qiskit_optimization/applications/optimization_application.py
index 9680695ce..32aae008e 100644
--- a/qiskit_optimization/applications/optimization_application.py
+++ b/qiskit_optimization/applications/optimization_application.py
@@ -1,6 +1,6 @@
 # This code is part of Qiskit.
 #
-# (C) Copyright IBM 2018, 2021.
+# (C) Copyright IBM 2018, 2022.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -11,13 +11,15 @@
 # that they have been altered from the originals.
 
 """An abstract class for optimization application classes."""
-from typing import Union, Dict
-from collections import OrderedDict
 from abc import ABC, abstractmethod
+from collections import OrderedDict
+from typing import Dict, Union
 
 import numpy as np
-
 from qiskit.opflow import StateFn
+from qiskit.quantum_info import Statevector
+from qiskit.result import QuasiDistribution
+
 from qiskit_optimization.algorithms import OptimizationResult
 from qiskit_optimization.problems.quadratic_program import QuadraticProgram
 
@@ -59,25 +61,45 @@ def _result_to_x(self, result: Union[OptimizationResult, np.ndarray]) -> np.ndar
         return x
 
     @staticmethod
-    def sample_most_likely(state_vector: Union[np.ndarray, Dict]) -> np.ndarray:
+    def sample_most_likely(
+        state_vector: Union[QuasiDistribution, Statevector, np.ndarray, Dict]
+    ) -> np.ndarray:
         """Compute the most likely binary string from state vector.
 
         Args:
-            state_vector: state vector or counts.
+            state_vector: state vector or counts or quasi-probabilities.
 
         Returns:
             binary string as numpy.ndarray of ints.
+
+        Raises:
+            ValueError: if state_vector is not QuasiDistribution, Statevector,
+                np.ndarray, or dict.
         """
-        if isinstance(state_vector, (OrderedDict, dict)):
+        if isinstance(state_vector, QuasiDistribution):
+            probabilities = state_vector.binary_probabilities()
+            binary_string = max(probabilities.items(), key=lambda kv: kv[1])[0]
+            x = np.asarray([int(y) for y in reversed(list(binary_string))])
+            return x
+        elif isinstance(state_vector, Statevector):
+            probabilities = state_vector.probabilities()
+            n = state_vector.num_qubits
+            k = np.argmax(np.abs(probabilities))
+            x = np.zeros(n)
+            for i in range(n):
+                x[i] = k % 2
+                k >>= 1
+            return x
+        elif isinstance(state_vector, (OrderedDict, dict)):
             # get the binary string with the largest count
-            binary_string = sorted(state_vector.items(), key=lambda kv: kv[1])[-1][0]
+            binary_string = max(state_vector.items(), key=lambda kv: kv[1])[0]
             x = np.asarray([int(y) for y in reversed(list(binary_string))])
             return x
         elif isinstance(state_vector, StateFn):
             binary_string = list(state_vector.sample().keys())[0]
             x = np.asarray([int(y) for y in reversed(list(binary_string))])
             return x
-        else:
+        elif isinstance(state_vector, np.ndarray):
             n = int(np.log2(state_vector.shape[0]))
             k = np.argmax(np.abs(state_vector))
             x = np.zeros(n)
@@ -85,3 +107,8 @@ def sample_most_likely(state_vector: Union[np.ndarray, Dict]) -> np.ndarray:
                 x[i] = k % 2
                 k >>= 1
             return x
+        else:
+            raise ValueError(
+                "state vector should be QuasiDistribution, Statevector, ndarray, or dict. "
+                f"But it is {type(state_vector)}."
+            )
diff --git a/qiskit_optimization/deprecation.py b/qiskit_optimization/deprecation.py
index c87ab4f7c..772d9791d 100644
--- a/qiskit_optimization/deprecation.py
+++ b/qiskit_optimization/deprecation.py
@@ -1,6 +1,6 @@
 # This code is part of Qiskit.
 #
-# (C) Copyright IBM 2021.
+# (C) Copyright IBM 2021, 2022.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
diff --git a/qiskit_optimization/problems/quadratic_program.py b/qiskit_optimization/problems/quadratic_program.py
index 57a98ab16..0aff7854b 100644
--- a/qiskit_optimization/problems/quadratic_program.py
+++ b/qiskit_optimization/problems/quadratic_program.py
@@ -1,6 +1,6 @@
 # This code is part of Qiskit.
 #
-# (C) Copyright IBM 2019, 2022.
+# (C) Copyright IBM 2019, 2023.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -23,6 +23,8 @@
 from docplex.mp.model_reader import ModelReader
 from numpy import ndarray
 from qiskit.opflow import OperatorBase
+from qiskit.quantum_info import SparsePauliOp
+from qiskit.quantum_info.operators.base_operator import BaseOperator
 from scipy.sparse import spmatrix
 
 import qiskit_optimization.optionals as _optionals
@@ -78,7 +80,7 @@ def __init__(self, name: str = "") -> None:
         self._objective = QuadraticObjective(self)
 
     def __repr__(self) -> str:
-        from ..translators.prettyprint import expr2str, DEFAULT_TRUNCATE
+        from ..translators.prettyprint import DEFAULT_TRUNCATE, expr2str
 
         objective = expr2str(
             constant=self._objective.constant,
@@ -1007,13 +1009,21 @@ def substitute_variables(
 
         return substitute_variables(self, constants, variables)
 
-    def to_ising(self) -> Tuple[OperatorBase, float]:
+    def to_ising(
+        self, opflow: Optional[bool] = None
+    ) -> Tuple[Union[OperatorBase, SparsePauliOp], float]:
         """Return the Ising Hamiltonian of this problem.
 
         Variables are mapped to qubits in the same order, i.e.,
         i-th variable is mapped to i-th qubit.
         See https://github.com/Qiskit/qiskit-terra/issues/1148 for details.
 
+        Args:
+            opflow: The output object is an OpFlow's operator if True.
+                Otherwise, it is ``SparsePauliOp``.
+                Refer to :func:`~qiskit_optimization.translators.to_ising`
+                for the default value.
+
         Returns:
             qubit_op: The qubit operator for the problem
             offset: The constant value in the Ising Hamiltonian.
@@ -1025,11 +1035,11 @@ def to_ising(self) -> Tuple[OperatorBase, float]:
         # pylint: disable=cyclic-import
         from ..translators.ising import to_ising
 
-        return to_ising(self)
+        return to_ising(self, opflow=opflow)
 
     def from_ising(
         self,
-        qubit_op: OperatorBase,
+        qubit_op: Union[OperatorBase, BaseOperator],
         offset: float = 0.0,
         linear: bool = False,
     ) -> None:
diff --git a/qiskit_optimization/problems/substitute_variables.py b/qiskit_optimization/problems/substitute_variables.py
index a09510b00..de96e58e0 100644
--- a/qiskit_optimization/problems/substitute_variables.py
+++ b/qiskit_optimization/problems/substitute_variables.py
@@ -1,6 +1,6 @@
 # This code is part of Qiskit.
 #
-# (C) Copyright IBM 2019, 2022.
+# (C) Copyright IBM 2019, 2023.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -114,7 +114,7 @@ class _SubstituteVariables:
     """A class to substitute variables of an optimization problem with constants for other
     variables"""
 
-    def __init__(self):
+    def __init__(self) -> None:
         self._src: Optional[QuadraticProgram] = None
         self._dst: Optional[QuadraticProgram] = None
         self._subs: Dict[str, SubstitutionExpression] = {}
diff --git a/qiskit_optimization/runtime/__init__.py b/qiskit_optimization/runtime/__init__.py
index 1b8a55bd0..be56033e1 100644
--- a/qiskit_optimization/runtime/__init__.py
+++ b/qiskit_optimization/runtime/__init__.py
@@ -1,6 +1,6 @@
 # This code is part of Qiskit.
 #
-# (C) Copyright IBM 2021.
+# (C) Copyright IBM 2021, 2023.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -11,11 +11,17 @@
 # that they have been altered from the originals.
 
 """
-Qiskit Optimization Runtime (:mod:`qiskit_optimization.runtime`)
-================================================================
+DEPRECATED Qiskit Optimization Runtime (:mod:`qiskit_optimization.runtime`)
+===========================================================================
 
 .. currentmodule:: qiskit_optimization.runtime
 
+.. warning::
+    This entire module is deprecated as of version 0.6.0 and will be removed no sooner than 3 months
+    after the release. Instead you should update your code to use the Qiskit Runtime Primitives. For
+    more details on how to migrate check out this guide, here: https://qisk.it/algo_migration#vqe
+    and https://qisk.it/algo_migration#qaoa !
+
 Programs that embed Qiskit Runtime in the algorithmic interfaces and facilitate usage of
 algorithms and scripts in the cloud.
 
@@ -23,25 +29,17 @@
    :toctree: ../stubs/
    :nosignatures:
 
-   VQEProgram
-   VQEProgramResult
    VQEClient
    VQERuntimeResult
-   QAOAProgram
    QAOAClient
 
 """
 
-from .vqe_program import VQEProgram, VQEProgramResult
-from .vqe_client import VQEClient, VQERuntimeResult
-from .qaoa_program import QAOAProgram
 from .qaoa_client import QAOAClient
+from .vqe_client import VQEClient, VQERuntimeResult
 
 __all__ = [
-    "VQEProgram",
-    "VQEProgramResult",
     "VQEClient",
     "VQERuntimeResult",
-    "QAOAProgram",
     "QAOAClient",
 ]
diff --git a/qiskit_optimization/runtime/qaoa_client.py b/qiskit_optimization/runtime/qaoa_client.py
index 0c9c6f176..05914df43 100644
--- a/qiskit_optimization/runtime/qaoa_client.py
+++ b/qiskit_optimization/runtime/qaoa_client.py
@@ -1,6 +1,6 @@
 # This code is part of Qiskit.
 #
-# (C) Copyright IBM 2021.
+# (C) Copyright IBM 2021, 2023.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -13,21 +13,23 @@
 """The Qiskit Optimization QAOA Quantum Program."""
 
 
-from typing import List, Callable, Optional, Any, Dict, Union
-import numpy as np
+from typing import Any, Callable, Dict, List, Optional, Union
 
+import numpy as np
 from qiskit import QuantumCircuit
 from qiskit.algorithms.optimizers import Optimizer
 from qiskit.opflow import OperatorBase
 from qiskit.providers import Provider
 from qiskit.providers.backend import Backend
 
+from qiskit_optimization.deprecation import DeprecatedType, warn_deprecated
 from qiskit_optimization.exceptions import QiskitOptimizationError
+
 from .vqe_client import VQEClient
 
 
 class QAOAClient(VQEClient):
-    """The Qiskit Optimization QAOA Runtime Client."""
+    """DEPRECATED The Qiskit Optimization QAOA Runtime Client."""
 
     def __init__(
         self,
@@ -97,6 +99,16 @@ class in Qiskit terra using a uniform distribution.
             QiskitOptimizationError: if optimization_level is not None and use_swap_strategies
                 is True.
         """
+        warn_deprecated(
+            "0.6.0",
+            DeprecatedType.CLASS,
+            "QAOAClient",
+            additional_msg=(
+                ". Instead you should use the new primitives based QAOA with the Qiskit IBM "
+                "Runtime Sampler primitive. For more details on how to migrate check out this guide, "
+                "here: https://qisk.it/algo_migration#qaoa"
+            ),
+        )
         if reps < 1:
             raise QiskitOptimizationError(f"reps must be greater than 0, received {reps}.")
 
diff --git a/qiskit_optimization/runtime/qaoa_program.py b/qiskit_optimization/runtime/qaoa_program.py
deleted file mode 100644
index 9c7719400..000000000
--- a/qiskit_optimization/runtime/qaoa_program.py
+++ /dev/null
@@ -1,111 +0,0 @@
-# This code is part of Qiskit.
-#
-# (C) Copyright IBM 2021.
-#
-# This code is licensed under the Apache License, Version 2.0. You may
-# obtain a copy of this license in the LICENSE.txt file in the root directory
-# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
-#
-# Any modifications or derivative works of this code must retain this
-# copyright notice, and modified files need to carry a notice indicating
-# that they have been altered from the originals.
-
-"""The Qiskit Optimization QAOA Quantum Program."""
-
-
-from typing import List, Callable, Optional, Any, Dict, Union
-import warnings
-import numpy as np
-
-from qiskit import QuantumCircuit
-from qiskit.providers import Provider
-from qiskit.providers.backend import Backend
-from qiskit.algorithms import MinimumEigensolverResult
-from qiskit.algorithms.optimizers import Optimizer
-from qiskit.opflow import OperatorBase
-
-from ..deprecation import warn_deprecated, DeprecatedType
-
-from .qaoa_client import QAOAClient
-from .vqe_program import VQEProgramResult
-
-
-class QAOAProgram(QAOAClient):
-    """DEPRECATED. This class has been renamed to ``qiskit_optimization.runtime.QAOAClient``."""
-
-    def __init__(
-        self,
-        optimizer: Optional[Union[Optimizer, Dict[str, Any]]] = None,
-        reps: int = 1,
-        initial_state: Optional[QuantumCircuit] = None,
-        mixer: Union[QuantumCircuit, OperatorBase] = None,
-        initial_point: Optional[np.ndarray] = None,
-        provider: Optional[Provider] = None,
-        backend: Optional[Backend] = None,
-        shots: int = 1024,
-        measurement_error_mitigation: bool = False,
-        callback: Optional[Callable[[int, np.ndarray, float, float], None]] = None,
-        store_intermediate: bool = False,
-    ) -> None:
-        """
-        Args:
-            optimizer: An optimizer or dictionary specifying a classical optimizer.
-                If a dictionary, only SPSA and QN-SPSA are supported. The dictionary must contain a
-                key ``name`` for the name of the optimizer and may contain additional keys for the
-                settings. E.g. ``{'name': 'SPSA', 'maxiter': 100}``.
-                Per default, SPSA is used.
-            reps: the integer parameter :math:`p` as specified in https://arxiv.org/abs/1411.4028,
-                Has a minimum valid value of 1.
-            initial_state: An optional initial state to prepend the QAOA circuit with
-            mixer: the mixer Hamiltonian to evolve with or a custom quantum circuit. Allows support
-                of optimizations in constrained subspaces as per https://arxiv.org/abs/1709.03489
-                as well as warm-starting the optimization as introduced
-                in http://arxiv.org/abs/2009.10095.
-            initial_point: An optional initial point (i.e. initial parameter values)
-                for the optimizer. If ``None`` a random vector is used.
-            provider: The provider.
-            backend: The backend to run the circuits on.
-            shots: The number of shots to be used
-            measurement_error_mitigation: Whether or not to use measurement error mitigation.
-            callback: a callback that can access the intermediate data during the optimization.
-                Four parameter values are passed to the callback as follows during each evaluation
-                by the optimizer for its current set of parameters as it works towards the minimum.
-                These are: the evaluation count, the optimizer parameters for the
-                ansatz, the evaluated mean and the evaluated standard deviation.
-            store_intermediate: Whether or not to store intermediate values of the optimization
-                steps. Per default False.
-        """
-        warn_deprecated(
-            version="0.3.0",
-            old_type=DeprecatedType.CLASS,
-            old_name="QAOAProgram",
-            new_name="QAOAClient",
-            additional_msg="from qiskit_optimization.runtime",
-        )
-
-        super().__init__(
-            optimizer=optimizer,
-            reps=reps,
-            initial_state=initial_state,
-            mixer=mixer,
-            initial_point=initial_point,
-            provider=provider,
-            backend=backend,
-            shots=shots,
-            measurement_error_mitigation=measurement_error_mitigation,
-            callback=callback,
-            store_intermediate=store_intermediate,
-        )
-
-    def compute_minimum_eigenvalue(
-        self, operator: OperatorBase, aux_operators: Optional[List[Optional[OperatorBase]]] = None
-    ) -> MinimumEigensolverResult:
-        result = super().compute_minimum_eigenvalue(operator, aux_operators)
-
-        # convert to previous result type
-        with warnings.catch_warnings():
-            warnings.filterwarnings("ignore", category=DeprecationWarning)
-            vqe_result = VQEProgramResult()
-
-        vqe_result.combine(result)
-        return vqe_result
diff --git a/qiskit_optimization/runtime/vqe_client.py b/qiskit_optimization/runtime/vqe_client.py
index df0837d46..8d0416e59 100644
--- a/qiskit_optimization/runtime/vqe_client.py
+++ b/qiskit_optimization/runtime/vqe_client.py
@@ -1,6 +1,6 @@
 # This code is part of Qiskit.
 #
-# (C) Copyright IBM 2021, 2022.
+# (C) Copyright IBM 2021, 2023.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -13,22 +13,24 @@
 """The Qiskit Optimization VQE Runtime Client."""
 
 
-from typing import List, Callable, Optional, Any, Dict, Union
 import warnings
-import numpy as np
+from typing import Any, Callable, Dict, List, Optional, Union
 
+import numpy as np
 from qiskit import QuantumCircuit
-from qiskit.exceptions import QiskitError
-from qiskit.providers import Provider
-from qiskit.providers.backend import Backend
 from qiskit.algorithms import MinimumEigensolver, MinimumEigensolverResult, VQEResult
-from qiskit.algorithms.optimizers import Optimizer, SPSA
+from qiskit.algorithms.optimizers import SPSA, Optimizer
+from qiskit.exceptions import QiskitError
 from qiskit.opflow import OperatorBase, PauliSumOp
+from qiskit.providers import Provider
+from qiskit.providers.backend import Backend, BackendV2
 from qiskit.quantum_info import SparsePauliOp
 
+from qiskit_optimization.deprecation import DeprecatedType, warn_deprecated
+
 
 class VQEClient(MinimumEigensolver):
-    """The Qiskit Optimization VQE Runtime Client to call the VQE runtime as a MinimumEigensolver.
+    """DEPRECATED The Qiskit Optimization VQE Runtime Client.
 
     This program is equivalent to the ``VQEClient`` in Qiskit Nature, but here also serves as
     basis for the Qiskit Optimization's ``QAOAClient``.
@@ -68,6 +70,16 @@ def __init__(
             store_intermediate: Whether or not to store intermediate values of the optimization
                 steps. Per default False.
         """
+        warn_deprecated(
+            "0.6.0",
+            DeprecatedType.CLASS,
+            "VQEClient",
+            additional_msg=(
+                ". Instead you should use the new primitives based SamplingVQE with the Qiskit IBM "
+                "Runtime Sampler primitive. For more details on how to migrate check out this guide, "
+                "here: https://qisk.it/algo_migration#vqe"
+            ),
+        )
         with warnings.catch_warnings():
             warnings.simplefilter("ignore")
             super().__init__()
@@ -297,7 +309,11 @@ def compute_minimum_eigenvalue(
         inputs = self.program_inputs(operator, aux_operators)
 
         # define runtime options
-        options = {"backend_name": self.backend.name()}
+        options = {
+            "backend_name": self.backend.name
+            if isinstance(self.backend, BackendV2)
+            else self.backend.name()
+        }
 
         # send job to runtime and return result
         job = self._send_job(inputs, options)
@@ -326,13 +342,14 @@ def compute_minimum_eigenvalue(
 
 
 class VQERuntimeResult(VQEResult):
-    """The VQEClient result object.
+    """DEPRECATED The VQEClient result object.
 
     This result objects contains the same as the VQEResult and additionally the history
     of the optimizer, containing information such as the function and parameter values per step.
     """
 
     def __init__(self) -> None:
+        warn_deprecated("0.6.0", DeprecatedType.CLASS, "VQERuntimeResult")
         super().__init__()
         self._job_id = None  # type: str
         self._optimizer_history = None  # type: Dict[str, Any]
diff --git a/qiskit_optimization/runtime/vqe_program.py b/qiskit_optimization/runtime/vqe_program.py
deleted file mode 100644
index c01df9182..000000000
--- a/qiskit_optimization/runtime/vqe_program.py
+++ /dev/null
@@ -1,125 +0,0 @@
-# This code is part of Qiskit.
-#
-# (C) Copyright IBM 2021.
-#
-# This code is licensed under the Apache License, Version 2.0. You may
-# obtain a copy of this license in the LICENSE.txt file in the root directory
-# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
-#
-# Any modifications or derivative works of this code must retain this
-# copyright notice, and modified files need to carry a notice indicating
-# that they have been altered from the originals.
-
-"""The Qiskit Optimization VQE Quantum Program."""
-
-
-from typing import List, Callable, Optional, Any, Dict, Union
-import warnings
-import numpy as np
-
-from qiskit import QuantumCircuit
-from qiskit.providers import Provider
-from qiskit.providers.backend import Backend
-from qiskit.algorithms import MinimumEigensolverResult
-from qiskit.algorithms.optimizers import Optimizer
-from qiskit.opflow import OperatorBase
-
-from ..deprecation import warn_deprecated, DeprecatedType
-
-from .vqe_client import VQEClient, VQERuntimeResult
-
-
-class VQEProgram(VQEClient):
-    """DEPRECATED. This class has been renamed to ``qiskit_optimization.runtime.VQEClient``.
-
-    This renaming reflects that this class is a client for a program executed in the cloud.
-    """
-
-    def __init__(
-        self,
-        ansatz: QuantumCircuit,
-        optimizer: Optional[Union[Optimizer, Dict[str, Any]]] = None,
-        initial_point: Optional[np.ndarray] = None,
-        provider: Optional[Provider] = None,
-        backend: Optional[Backend] = None,
-        shots: int = 1024,
-        measurement_error_mitigation: bool = False,
-        callback: Optional[Callable[[int, np.ndarray, float, float], None]] = None,
-        store_intermediate: bool = False,
-    ) -> None:
-        """
-        Args:
-            ansatz: A parameterized circuit used as Ansatz for the wave function.
-            optimizer: An optimizer or dictionary specifying a classical optimizer.
-                If a dictionary, only SPSA and QN-SPSA are supported. The dictionary must contain a
-                key ``name`` for the name of the optimizer and may contain additional keys for the
-                settings. E.g. ``{'name': 'SPSA', 'maxiter': 100}``.
-                Per default, SPSA is used.
-            backend: The backend to run the circuits on.
-            initial_point: An optional initial point (i.e. initial parameter values)
-                for the optimizer. If ``None`` a random vector is used.
-            provider: Provider that supports the runtime feature.
-            shots: The number of shots to be used
-            measurement_error_mitigation: Whether or not to use measurement error mitigation.
-            callback: a callback that can access the intermediate data during the optimization.
-                Four parameter values are passed to the callback as follows during each evaluation
-                by the optimizer for its current set of parameters as it works towards the minimum.
-                These are: the evaluation count, the optimizer parameters for the
-                ansatz, the evaluated mean and the evaluated standard deviation.
-            store_intermediate: Whether or not to store intermediate values of the optimization
-                steps. Per default False.
-        """
-        warn_deprecated(
-            version="0.3.0",
-            old_type=DeprecatedType.CLASS,
-            old_name="VQEProgram",
-            new_name="VQEClient",
-            additional_msg="from qiskit_optimization.runtime",
-        )
-
-        super().__init__(
-            ansatz,
-            optimizer,
-            initial_point,
-            provider,
-            backend,
-            shots,
-            measurement_error_mitigation,
-            callback,
-            store_intermediate,
-        )
-
-    @classmethod
-    def supports_aux_operators(cls) -> bool:
-        return True
-
-    def compute_minimum_eigenvalue(
-        self, operator: OperatorBase, aux_operators: Optional[List[Optional[OperatorBase]]] = None
-    ) -> MinimumEigensolverResult:
-        result = super().compute_minimum_eigenvalue(operator, aux_operators)
-
-        # convert to previous result type
-        with warnings.catch_warnings():
-            warnings.filterwarnings("ignore", category=DeprecationWarning)
-            vqe_result = VQEProgramResult()
-
-        vqe_result.combine(result)
-        return vqe_result
-
-
-class VQEProgramResult(VQERuntimeResult):
-    """DEPRECATED. The ``VQEProgram`` result object has been renamed to ``VQERuntimeResult``.
-
-    This result objects contains the same as the VQEResult and additionally the history
-    of the optimizer, containing information such as the function and parameter values per step.
-    """
-
-    def __init__(self) -> None:
-        super().__init__()
-        warn_deprecated(
-            version="0.3.0",
-            old_type=DeprecatedType.CLASS,
-            old_name="VQEProgramResult",
-            new_name="VQERuntimeResult",
-            additional_msg="from qiskit_optimization.runtime",
-        )
diff --git a/qiskit_optimization/translators/ising.py b/qiskit_optimization/translators/ising.py
index ee8768e0b..52cdf39a9 100644
--- a/qiskit_optimization/translators/ising.py
+++ b/qiskit_optimization/translators/ising.py
@@ -1,6 +1,6 @@
 # This code is part of Qiskit.
 #
-# (C) Copyright IBM 2019, 2022.
+# (C) Copyright IBM 2019, 2023.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -13,25 +13,36 @@
 """Translator between an Ising Hamiltonian and a quadratic program"""
 
 import math
-from typing import Tuple
+from typing import Optional, Tuple, Union
+from warnings import warn
 
 import numpy as np
+from qiskit.opflow import ListOp, OperatorBase, PauliOp, PauliSumOp
+from qiskit.quantum_info import Pauli, SparsePauliOp
+from qiskit.quantum_info.operators.base_operator import BaseOperator
 
-from qiskit.opflow import I, ListOp, OperatorBase, PauliOp, PauliSumOp, SummedOp
-from qiskit.quantum_info import Pauli
 from qiskit_optimization.exceptions import QiskitOptimizationError
 from qiskit_optimization.problems.quadratic_program import QuadraticProgram
 
 
-def to_ising(quad_prog: QuadraticProgram) -> Tuple[OperatorBase, float]:
+def to_ising(
+    quad_prog: QuadraticProgram, opflow: Optional[bool] = None
+) -> Tuple[Union[PauliSumOp, SparsePauliOp], float]:
     """Return the Ising Hamiltonian of this problem.
 
     Variables are mapped to qubits in the same order, i.e.,
     i-th variable is mapped to i-th qubit.
     See https://github.com/Qiskit/qiskit-terra/issues/1148 for details.
 
+    .. note::
+
+        The default value of ``opflow`` argument is currently set ``True``, but it will
+        first be changed to ``False`` and then deprecated in future releases.
+
     Args:
         quad_prog: The problem to be translated.
+        opflow: The output object is a ``PauliSumOp`` operator if True.
+            Otherwise, it is ``SparsePauliOp``. (default: True)
 
     Returns:
         A tuple (qubit_op, offset) comprising the qubit operator for the problem
@@ -42,6 +53,15 @@ def to_ising(quad_prog: QuadraticProgram) -> Tuple[OperatorBase, float]:
             in the problem.
         QiskitOptimizationError: If constraints exist in the problem.
     """
+    if opflow is None:
+        opflow = True
+        warn(
+            "`opflow` argument of `to_ising` is not set explicitly. "
+            "It is currently set True, but the default value will be changed to False. "
+            "We suggest using `SparsePauliOp` instead of Opflow operators.",
+            stacklevel=2,
+        )
+
     # if problem has variables that are not binary, raise an error
     if quad_prog.get_num_vars() > quad_prog.get_num_binary_vars():
         raise QiskitOptimizationError(
@@ -61,10 +81,10 @@ def to_ising(quad_prog: QuadraticProgram) -> Tuple[OperatorBase, float]:
         )
 
     # initialize Hamiltonian.
-    num_nodes = quad_prog.get_num_vars()
+    num_vars = quad_prog.get_num_vars()
     pauli_list = []
     offset = 0.0
-    zero = np.zeros(num_nodes, dtype=bool)
+    zero = np.zeros(num_vars, dtype=bool)
 
     # set a sign corresponding to a maximized or minimized problem.
     # sign == 1 is for minimized problem. sign == -1 is for maximized problem.
@@ -79,7 +99,7 @@ def to_ising(quad_prog: QuadraticProgram) -> Tuple[OperatorBase, float]:
         weight = coef * sense / 2
         z_p[idx] = True
 
-        pauli_list.append(PauliOp(Pauli((z_p, zero)), -weight))
+        pauli_list.append(SparsePauliOp(Pauli((z_p, zero)), -weight))
         offset += weight
 
     # create Pauli terms
@@ -92,36 +112,35 @@ def to_ising(quad_prog: QuadraticProgram) -> Tuple[OperatorBase, float]:
             z_p = zero.copy()
             z_p[i] = True
             z_p[j] = True
-            pauli_list.append(PauliOp(Pauli((z_p, zero)), weight))
+            pauli_list.append(SparsePauliOp(Pauli((z_p, zero)), weight))
 
         z_p = zero.copy()
         z_p[i] = True
-        pauli_list.append(PauliOp(Pauli((z_p, zero)), -weight))
+        pauli_list.append(SparsePauliOp(Pauli((z_p, zero)), -weight))
 
         z_p = zero.copy()
         z_p[j] = True
-        pauli_list.append(PauliOp(Pauli((z_p, zero)), -weight))
+        pauli_list.append(SparsePauliOp(Pauli((z_p, zero)), -weight))
 
         offset += weight
 
-    # Remove paulis whose coefficients are zeros.
-    qubit_op = sum(pauli_list)
-
-    # qubit_op could be the integer 0, in this case return an identity operator of
-    # appropriate size
-    if isinstance(qubit_op, OperatorBase):
-        qubit_op = qubit_op.reduce()
+    if pauli_list:
+        # Remove paulis whose coefficients are zeros.
+        qubit_op = sum(pauli_list).simplify(atol=0)
     else:
         # If there is no variable, we set num_nodes=1 so that qubit_op should be an operator.
         # If num_nodes=0, I^0 = 1 (int).
-        num_nodes = max(1, num_nodes)
-        qubit_op = 0 * I ^ num_nodes
+        num_vars = max(1, num_vars)
+        qubit_op = SparsePauliOp("I" * num_vars, 0)
+
+    if opflow:
+        qubit_op = PauliSumOp(qubit_op)
 
     return qubit_op, offset
 
 
 def from_ising(
-    qubit_op: OperatorBase,
+    qubit_op: Union[OperatorBase, BaseOperator],
     offset: float = 0.0,
     linear: bool = False,
 ) -> QuadraticProgram:
@@ -131,6 +150,13 @@ def from_ising(
     i-th variable is mapped to i-th qubit.
     See https://github.com/Qiskit/qiskit-terra/issues/1148 for details.
 
+    .. note::
+
+        The ``qubit_op`` argument can currently accept Opflow operators (``OperatorBase`` type),
+        but have been superseded by Qiskit Terra quantum_info ``BaseOperators`` such as
+        ``SparsePauliOp``. Opflow operator support will be deprecated in a future release
+        and subsequently removed after that.
+
     Args:
         qubit_op: The qubit operator of the problem.
         offset: The constant term in the Ising Hamiltonian.
@@ -148,9 +174,25 @@ def from_ising(
         QiskitOptimizationError: if any Pauli term has an imaginary coefficient
         NotImplementedError: If the input operator is a ListOp
     """
-    if isinstance(qubit_op, PauliSumOp):
-        qubit_op = qubit_op.to_pauli_op()
+    # quantum_info
+    if isinstance(qubit_op, BaseOperator):
+        if not isinstance(qubit_op, SparsePauliOp):
+            qubit_op = SparsePauliOp(qubit_op)
 
+    # opflow
+    if isinstance(qubit_op, OperatorBase):
+        warn(
+            "The `qubit_op` argument can currently accept Opflow operators (`OperatorBase` type), "
+            "but have been superseded by Qiskit Terra quantum_info `BaseOperators` such as "
+            "`SparsePauliOp`. Opflow operator support will be deprecated in a future release "
+            "and subsequently removed after that.",
+            category=PendingDeprecationWarning,
+            stacklevel=2,
+        )
+    if isinstance(qubit_op, PauliSumOp):
+        qubit_op = qubit_op.primitive * qubit_op.coeff
+    if isinstance(qubit_op, PauliOp):
+        qubit_op = SparsePauliOp(qubit_op.primitive, qubit_op.coeff)
     # No support for ListOp yet, this can be added in future
     # pylint: disable=unidiomatic-typecheck
     if type(qubit_op) == ListOp:
@@ -162,21 +204,15 @@ def from_ising(
     quad_prog = QuadraticProgram()
     quad_prog.binary_var_list(qubit_op.num_qubits)
 
-    if not isinstance(qubit_op, SummedOp):
-        pauli_list = [qubit_op.to_pauli_op()]
-    else:
-        pauli_list = qubit_op.to_pauli_op()
-
     # prepare a matrix of coefficients of Pauli terms
     # `pauli_coeffs_diag` is the diagonal part
     # `pauli_coeffs_triu` is the upper triangular part
     pauli_coeffs_diag = [0.0] * qubit_op.num_qubits
     pauli_coeffs_triu = {}
 
-    for pauli_op in pauli_list:
-        pauli_op = pauli_op.to_pauli_op()
-        pauli = pauli_op.primitive
-        coeff = pauli_op.coeff
+    for pauli_op in qubit_op:
+        pauli = pauli_op.paulis[0]
+        coeff = pauli_op.coeffs[0]
 
         if not math.isclose(coeff.imag, 0.0, abs_tol=1e-10):
             raise QiskitOptimizationError(f"Imaginary coefficient exists: {pauli_op}")
diff --git a/releasenotes/notes/0.5/add-primitives-support-31af39549b5e66e3.yaml b/releasenotes/notes/0.5/add-primitives-support-31af39549b5e66e3.yaml
new file mode 100644
index 000000000..94d48700d
--- /dev/null
+++ b/releasenotes/notes/0.5/add-primitives-support-31af39549b5e66e3.yaml
@@ -0,0 +1,24 @@
+---
+prelude: >
+    Qiskit Optimization 0.5 supports the new algorithms introduced in Qiskit Terra 0.22
+    which in turn rely on the `Qiskit Primitives <https://qiskit.org/documentation/apidoc/primitives.html>`_.
+    Qiskit Optimization 0.5 still supports the former algorithms based on :class:`qiskit.utils.QuantumInstance`,
+    but they will be deprecated and then removed, along with the support here, in future releases.
+
+features:
+  - |
+    The :class:`~.MinimumEigenOptimizer` class takes the primitives-based algorithms
+    (:class:`qiskit.algorithms.minimum_eigensolvers.SamplingMinimumEigensolver` and
+    :class:`qiskit.algorithms.minimum_eigensolvers.NumPyMinimumEigensolver`)
+    as ``min_eigen_solver`` argument.
+    The former algorithm :class:`qiskit.algorithms.MinimumEigensolver`
+    is pending deprecation and will be deprecated and subsequently removed in future releases.
+    Note that :class:`qiskit.algorithms.minimum_eigensolvers.SamplingVQE` supersedes
+    :class:`qiskit.algorithms.VQE` for :class:`~.MinimumEigenOptimizer`.
+    :class:`qiskit.algorithms.minimum_eigensolvers.NumPyMinimumEigensolver` also supersedes
+    :class:`qiskit.algorithms.NumPyMinimumEigensolver`.
+  - |
+    The :class:`~.WarmStartQAOAOptimizer` class takes the primitives-based QAOA
+    (:class:`qiskit.algorithms.minimum_eigensolvers.QAOA`) as ``qaoa`` argument.
+    The former algorithm :class:`qiskit.algorithms.QAOA`
+    is pending deprecation and will be deprecated and subsequently removed in future releases.
diff --git a/releasenotes/notes/fix-tsplib-parser-5ae73dc6233eed33.yaml b/releasenotes/notes/0.5/fix-tsplib-parser-5ae73dc6233eed33.yaml
similarity index 100%
rename from releasenotes/notes/fix-tsplib-parser-5ae73dc6233eed33.yaml
rename to releasenotes/notes/0.5/fix-tsplib-parser-5ae73dc6233eed33.yaml
diff --git a/releasenotes/notes/0.5/grover-opt-primitive-de82d051d6cee2e4.yaml b/releasenotes/notes/0.5/grover-opt-primitive-de82d051d6cee2e4.yaml
new file mode 100644
index 000000000..7aea9b34a
--- /dev/null
+++ b/releasenotes/notes/0.5/grover-opt-primitive-de82d051d6cee2e4.yaml
@@ -0,0 +1,9 @@
+---
+features:
+  - |
+    The :class:`~.GroverOptimizer` class has a new keyword argument, ``sampler`` which is
+    used to run the algorithm using an instance of the :class:`qiskit.primitives.BaseSampler`
+    interface to calculate the results. This new argument supersedes the
+    the ``quantum_instance`` argument and accordingly, ``quantum_instance``
+    is pending deprecation and will be deprecated and subsequently removed in
+    future releases.
diff --git a/releasenotes/notes/0.5/remove-vqe-qaoa-programs-152a997734296fe2.yaml b/releasenotes/notes/0.5/remove-vqe-qaoa-programs-152a997734296fe2.yaml
new file mode 100644
index 000000000..82a5849ec
--- /dev/null
+++ b/releasenotes/notes/0.5/remove-vqe-qaoa-programs-152a997734296fe2.yaml
@@ -0,0 +1,5 @@
+---
+upgrade:
+  - |
+    The previously deprecated ``VQEProgram`` and ``QAOAProgram`` classes have been removed.
+    They were originally deprecated in the Qiskit Optimization 0.3.0 release.
diff --git a/releasenotes/notes/add-python311-support-b44aa96b718e7914.yaml b/releasenotes/notes/add-python311-support-b44aa96b718e7914.yaml
new file mode 100644
index 000000000..f882dde61
--- /dev/null
+++ b/releasenotes/notes/add-python311-support-b44aa96b718e7914.yaml
@@ -0,0 +1,5 @@
+---
+upgrade:
+  - |
+    Added support for running with Python 3.11.
+    At the the time of the release, CPLEX didn't have a python 3.11 version.
diff --git a/releasenotes/notes/deprecate-vqe-client-de93fff8c4645802.yaml b/releasenotes/notes/deprecate-vqe-client-de93fff8c4645802.yaml
new file mode 100644
index 000000000..5f5079682
--- /dev/null
+++ b/releasenotes/notes/deprecate-vqe-client-de93fff8c4645802.yaml
@@ -0,0 +1,7 @@
+---
+deprecations:
+  - |
+    The :class:`.VQEClient`, :class:`.QAOAClient`, and :class:`.VQERuntimeResult`
+    are now deprecated. Instead, users should migrate their code to use the Qiskit
+    Runtime Primitives. Guides on how to use this can be found
+    `here (VQE) <https://qisk.it/algo_migration#vqe>`_ and `here (QAOA) <https://qisk.it/algo_migration#qaoa>`_.
diff --git a/releasenotes/notes/runtimeclient-v2compat-c5b5541401cc6405.yaml b/releasenotes/notes/runtimeclient-v2compat-c5b5541401cc6405.yaml
new file mode 100644
index 000000000..1c64e32ba
--- /dev/null
+++ b/releasenotes/notes/runtimeclient-v2compat-c5b5541401cc6405.yaml
@@ -0,0 +1,8 @@
+---
+fixes:
+  - |
+    Fix support of ``BackendV2`` in the :class:`.VQEClient`. Previously, backends instantiated
+    with the ``IBMProvider`` failed since they return backends of type ``BackendV2``, which
+    were not correctly supported in the VQE client. Backends instantiated with the ``IBMQ``
+    provider continue to work as before.
+
diff --git a/releasenotes/notes/support-sprase-pauli-op-00d29000d48c93ca.yaml b/releasenotes/notes/support-sprase-pauli-op-00d29000d48c93ca.yaml
new file mode 100644
index 000000000..d8aa2d006
--- /dev/null
+++ b/releasenotes/notes/support-sprase-pauli-op-00d29000d48c93ca.yaml
@@ -0,0 +1,14 @@
+---
+features:
+  - |
+    Updated :func:`~.to_ising` to support optionally returning ``qiskit.quantum_info.SparsePauliOp``.
+    This is achieved by a new ``opflow`` argument to :func:`~.to_ising` to control if the output is
+    the Opflow operator, as has been done in the past,  (if ``True``) or ``SparsePauliOp`` (if ``False``).
+    The default value of ``opflow`` argument is currently ``True``, but it will
+    first be changed to ``False``. The parameter will be deprecated and removed in a future release.
+  - |
+    Updated :func:`~.from_ising` to support optionally accepting ``qiskit.quantum_info.SparsePauliOp``.
+    The ``qubit_op`` argument can currently accept the Opflow operators (``OperatorBase`` type),
+    but have been superseded by Qiskit Terra quantum_info ``BaseOperators`` such as
+    ``SparsePauliOp``. The Opflow operator support will be deprecated in a future release
+    and subsequently removed after that.
diff --git a/requirements-dev.txt b/requirements-dev.txt
index ca98dc8c5..1b096ce71 100644
--- a/requirements-dev.txt
+++ b/requirements-dev.txt
@@ -1,15 +1,14 @@
 coverage>=4.4.0
 matplotlib>=2.1
 black[jupyter]~=22.0
-pylint>=2.8.3,<2.14.0
+pylint>=2.16.2
 pylatexenc>=1.4
 stestr>=2.0.0
 ddt>=1.2.0,!=1.4.0
 reno>=3.4.0
-Sphinx>=1.8.3,!=3.1.0,!=5.2.0,!=5.2.0.post0
+Sphinx>=5.0
 sphinx-design>=0.2.0
 sphinx-gallery
-sphinx-autodoc-typehints<1.14.0
 sphinxcontrib-spelling
 jupyter-sphinx
 discover
@@ -17,4 +16,4 @@ qiskit-aer>=0.11
 mypy>=0.981
 mypy-extensions>=0.4.3
 nbsphinx
-qiskit_sphinx_theme
+qiskit_sphinx_theme>=1.10.*
diff --git a/requirements.txt b/requirements.txt
index 7f3226a7c..32d95162f 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,6 +1,6 @@
-qiskit-terra>=0.20.0
+qiskit-terra>=0.22.4
 scipy>=1.4
 numpy>=1.17
-docplex>=2.21.207
+docplex>=2.21.207,!=2.24.231
 setuptools>=40.1.0
-networkx>=2.2
+networkx>=2.6.3
diff --git a/setup.py b/setup.py
index 9994d8e38..536857cfc 100644
--- a/setup.py
+++ b/setup.py
@@ -1,6 +1,6 @@
 # This code is part of Qiskit.
 #
-# (C) Copyright IBM 2021, 2022.
+# (C) Copyright IBM 2021, 2023.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -16,12 +16,16 @@
 import os
 import re
 
-with open('requirements.txt') as f:
+with open("requirements.txt") as f:
     REQUIREMENTS = f.read().splitlines()
 
-if not hasattr(setuptools, 'find_namespace_packages') or not inspect.ismethod(setuptools.find_namespace_packages):
-    print("Your setuptools version:'{}' does not support PEP 420 (find_namespace_packages). "
-          "Upgrade it to version >='40.1.0' and repeat install.".format(setuptools.__version__))
+if not hasattr(setuptools, "find_namespace_packages") or not inspect.ismethod(
+    setuptools.find_namespace_packages
+):
+    print(
+        "Your setuptools version:'{}' does not support PEP 420 (find_namespace_packages). "
+        "Upgrade it to version >='40.1.0' and repeat install.".format(setuptools.__version__)
+    )
     sys.exit(1)
 
 VERSION_PATH = os.path.join(os.path.dirname(__file__), "qiskit_optimization", "VERSION.txt")
@@ -39,15 +43,15 @@
     )
 
 setuptools.setup(
-    name='qiskit-optimization',
+    name="qiskit-optimization",
     version=VERSION,
-    description='Qiskit Optimization: A library of quantum computing optimizations',
+    description="Qiskit Optimization: A library of quantum computing optimizations",
     long_description=README,
     long_description_content_type="text/markdown",
-    url='https://github.com/Qiskit/qiskit-optimization',
-    author='Qiskit Optimization Development Team',
-    author_email='hello@qiskit.org',
-    license='Apache-2.0',
+    url="https://github.com/Qiskit/qiskit-optimization",
+    author="Qiskit Optimization Development Team",
+    author_email="hello@qiskit.org",
+    license="Apache-2.0",
     classifiers=[
         "Environment :: Console",
         "License :: OSI Approved :: Apache Software License",
@@ -61,19 +65,20 @@
         "Programming Language :: Python :: 3.8",
         "Programming Language :: Python :: 3.9",
         "Programming Language :: Python :: 3.10",
-        "Topic :: Scientific/Engineering"
+        "Programming Language :: Python :: 3.11",
+        "Topic :: Scientific/Engineering",
     ],
-    keywords='qiskit sdk quantum optimization',
-    packages=setuptools.find_packages(include=['qiskit_optimization', 'qiskit_optimization.*']),
+    keywords="qiskit sdk quantum optimization",
+    packages=setuptools.find_packages(include=["qiskit_optimization", "qiskit_optimization.*"]),
     install_requires=REQUIREMENTS,
     include_package_data=True,
     python_requires=">=3.7",
     extras_require={
-        'cplex': ['cplex'],
-        'cvx': ['cvxpy'],
-        'matplotlib': ['matplotlib'],
-        'gurobi': ['gurobipy'],
-        'scipy': ['scipy>=1.9']
+        "cplex": ["cplex;python_version < '3.11'"],
+        "cvx": ["cvxpy"],
+        "matplotlib": ["matplotlib"],
+        "gurobi": ["gurobipy"],
+        "scipy": ["scipy>=1.9;python_version >= '3.8'"],
     },
-    zip_safe=False
+    zip_safe=False,
 )
diff --git a/test/algorithms/legacy/test_min_eigen_optimizer.py b/test/algorithms/legacy/test_min_eigen_optimizer.py
new file mode 100644
index 000000000..e6e99109f
--- /dev/null
+++ b/test/algorithms/legacy/test_min_eigen_optimizer.py
@@ -0,0 +1,406 @@
+# This code is part of Qiskit.
+#
+# (C) Copyright IBM 2018, 2022.
+#
+# This code is licensed under the Apache License, Version 2.0. You may
+# obtain a copy of this license in the LICENSE.txt file in the root directory
+# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
+#
+# Any modifications or derivative works of this code must retain this
+# copyright notice, and modified files need to carry a notice indicating
+# that they have been altered from the originals.
+
+""" Test Min Eigen Optimizer with the legacy MinimumEigensolver"""
+
+import unittest
+from test.optimization_test_case import QiskitOptimizationTestCase
+
+from test.runtime.fake_vqeruntime import FakeVQERuntimeProvider, FakeQAOARuntimeProvider
+
+import numpy as np
+from ddt import data, ddt
+from qiskit import BasicAer
+from qiskit.algorithms import QAOA, VQE, NumPyMinimumEigensolver
+from qiskit.algorithms.optimizers import COBYLA, SPSA
+from qiskit.circuit.library import TwoLocal
+from qiskit.providers.basicaer import QasmSimulatorPy
+from qiskit.utils import QuantumInstance, algorithm_globals
+import qiskit_optimization.optionals as _optionals
+from qiskit_optimization.algorithms import (
+    CplexOptimizer,
+    MinimumEigenOptimizer,
+    MinimumEigenOptimizationResult,
+)
+from qiskit_optimization.algorithms.optimization_algorithm import (
+    OptimizationResultStatus,
+)
+from qiskit_optimization.converters import (
+    InequalityToEquality,
+    IntegerToBinary,
+    LinearEqualityToPenalty,
+    MaximizeToMinimize,
+    QuadraticProgramToQubo,
+)
+from qiskit_optimization.problems import QuadraticProgram
+from qiskit_optimization.runtime import VQEClient, QAOAClient
+
+
+@ddt
+class TestMinEigenOptimizer(QiskitOptimizationTestCase):
+    """Min Eigen Optimizer Tests."""
+
+    def setUp(self):
+        super().setUp()
+
+        # setup minimum eigen solvers
+        self.min_eigen_solvers = {}
+
+        # exact eigen solver
+        with self.assertWarns(PendingDeprecationWarning):
+            self.min_eigen_solvers["exact"] = NumPyMinimumEigensolver()
+
+            # QAOA
+            optimizer = COBYLA()
+            self.min_eigen_solvers["qaoa"] = QAOA(optimizer=optimizer)
+            # simulators
+            self.sv_simulator = QuantumInstance(
+                BasicAer.get_backend("statevector_simulator"),
+                seed_simulator=123,
+                seed_transpiler=123,
+            )
+            self.qasm_simulator = QuantumInstance(
+                BasicAer.get_backend("qasm_simulator"),
+                seed_simulator=123,
+                seed_transpiler=123,
+            )
+        # test minimize
+        self.op_minimize = QuadraticProgram()
+        self.op_minimize.integer_var(0, 3, "x")
+        self.op_minimize.binary_var("y")
+        self.op_minimize.minimize(linear={"x": 1, "y": 2})
+        self.op_minimize.linear_constraint(linear={"x": 1, "y": 1}, sense=">=", rhs=1, name="xy")
+
+        # test maximize
+        self.op_maximize = QuadraticProgram()
+        self.op_maximize.integer_var(0, 3, "x")
+        self.op_maximize.binary_var("y")
+        self.op_maximize.maximize(linear={"x": 1, "y": 2})
+        self.op_maximize.linear_constraint(linear={"x": 1, "y": 1}, sense="<=", rhs=1, name="xy")
+
+        # test bit ordering
+        self.op_ordering = QuadraticProgram("bit ordering")
+        self.op_ordering.binary_var("x")
+        self.op_ordering.binary_var("y")
+        self.op_ordering.minimize(linear={"x": 1, "y": -2})
+
+    @data(
+        ("exact", None, "op_ip1.lp"),
+        ("qaoa", "statevector_simulator", "op_ip1.lp"),
+        ("qaoa", "qasm_simulator", "op_ip1.lp"),
+    )
+    @unittest.skipIf(not _optionals.HAS_CPLEX, "CPLEX not available.")
+    def test_min_eigen_optimizer(self, config):
+        """Min Eigen Optimizer Test"""
+        try:
+            # unpack configuration
+            min_eigen_solver_name, backend, filename = config
+
+            # get minimum eigen solver
+            min_eigen_solver = self.min_eigen_solvers[min_eigen_solver_name]
+            if backend:
+                min_eigen_solver.quantum_instance = BasicAer.get_backend(backend)
+
+            # construct minimum eigen optimizer
+            min_eigen_optimizer = MinimumEigenOptimizer(min_eigen_solver)
+
+            # load optimization problem
+            problem = QuadraticProgram()
+            lp_file = self.get_resource_path(filename, "algorithms/resources")
+            problem.read_from_lp_file(lp_file)
+
+            # solve problem with cplex
+            cplex = CplexOptimizer(cplex_parameters={"threads": 1, "randomseed": 1})
+            cplex_result = cplex.solve(problem)
+
+            # solve problem
+            with self.assertWarns(PendingDeprecationWarning):
+                result = min_eigen_optimizer.solve(problem)
+            self.assertIsNotNone(result)
+
+            # analyze results
+            self.assertAlmostEqual(cplex_result.fval, result.fval)
+
+            # check that eigensolver result is present
+            self.assertIsNotNone(result.min_eigen_solver_result)
+        except RuntimeError as ex:
+            self.fail(str(ex))
+
+    @data(
+        ("op_ip1.lp", -470, 12, OptimizationResultStatus.SUCCESS),
+        ("op_ip1.lp", np.inf, None, OptimizationResultStatus.FAILURE),
+    )
+    @unittest.skipIf(not _optionals.HAS_CPLEX, "CPLEX not available.")
+    def test_min_eigen_optimizer_with_filter(self, config):
+        """Min Eigen Optimizer Test"""
+        try:
+            # unpack configuration
+            filename, lowerbound, fval, status = config
+
+            # get minimum eigen solver
+            with self.assertWarns(PendingDeprecationWarning):
+                min_eigen_solver = NumPyMinimumEigensolver()
+
+            # set filter
+            # pylint: disable=unused-argument
+            def filter_criterion(x, v, aux):
+                return v > lowerbound
+
+            min_eigen_solver.filter_criterion = filter_criterion
+
+            # construct minimum eigen optimizer
+            min_eigen_optimizer = MinimumEigenOptimizer(min_eigen_solver)
+
+            # load optimization problem
+            problem = QuadraticProgram()
+            lp_file = self.get_resource_path(filename, "algorithms/resources")
+            problem.read_from_lp_file(lp_file)
+
+            # solve problem
+            with self.assertWarns(PendingDeprecationWarning):
+                result = min_eigen_optimizer.solve(problem)
+            self.assertIsNotNone(result)
+
+            # analyze results
+            self.assertAlmostEqual(fval, result.fval)
+            self.assertEqual(status, result.status)
+
+            # check that eigensolver result is present
+            self.assertIsNotNone(result.min_eigen_solver_result)
+        except RuntimeError as ex:
+            self.fail(str(ex))
+
+    def test_converter_list(self):
+        """Test converter list"""
+        op = QuadraticProgram()
+        op.integer_var(0, 3, "x")
+        op.binary_var("y")
+
+        op.maximize(linear={"x": 1, "y": 2})
+        op.linear_constraint(linear={"x": 1, "y": 1}, sense="LE", rhs=3, name="xy_leq")
+        with self.assertWarns(PendingDeprecationWarning):
+            min_eigen_solver = NumPyMinimumEigensolver()
+        # a single converter
+        qp2qubo = QuadraticProgramToQubo()
+        min_eigen_optimizer = MinimumEigenOptimizer(min_eigen_solver, converters=qp2qubo)
+        with self.assertWarns(PendingDeprecationWarning):
+            result = min_eigen_optimizer.solve(op)
+        self.assertEqual(result.fval, 4)
+        # a list of converters
+        ineq2eq = InequalityToEquality()
+        int2bin = IntegerToBinary()
+        penalize = LinearEqualityToPenalty()
+        max2min = MaximizeToMinimize()
+        converters = [ineq2eq, int2bin, penalize, max2min]
+        min_eigen_optimizer = MinimumEigenOptimizer(min_eigen_solver, converters=converters)
+        with self.assertWarns(PendingDeprecationWarning):
+            result = min_eigen_optimizer.solve(op)
+        self.assertEqual(result.fval, 4)
+        with self.assertRaises(TypeError):
+            invalid = [qp2qubo, "invalid converter"]
+            MinimumEigenOptimizer(min_eigen_solver, converters=invalid)
+
+    def test_samples_numpy_eigen_solver(self):
+        """Test samples for NumPyMinimumEigensolver"""
+        # test minimize
+        with self.assertWarns(PendingDeprecationWarning):
+            min_eigen_solver = NumPyMinimumEigensolver()
+        min_eigen_optimizer = MinimumEigenOptimizer(min_eigen_solver)
+        with self.assertWarns(PendingDeprecationWarning):
+            result = min_eigen_optimizer.solve(self.op_minimize)
+        opt_sol = 1
+        success = OptimizationResultStatus.SUCCESS
+        self.assertEqual(result.fval, opt_sol)
+        self.assertEqual(len(result.samples), 1)
+        np.testing.assert_array_almost_equal(result.samples[0].x, [1, 0])
+        self.assertAlmostEqual(result.samples[0].fval, opt_sol)
+        self.assertAlmostEqual(result.samples[0].probability, 1.0)
+        self.assertEqual(result.samples[0].status, success)
+        self.assertEqual(len(result.raw_samples), 1)
+        np.testing.assert_array_almost_equal(result.raw_samples[0].x, [1, 0, 0, 0, 0])
+        self.assertAlmostEqual(result.raw_samples[0].fval, opt_sol)
+        self.assertAlmostEqual(result.raw_samples[0].probability, 1.0)
+        self.assertEqual(result.raw_samples[0].status, success)
+        # test maximize
+        with self.assertWarns(PendingDeprecationWarning):
+            min_eigen_solver = NumPyMinimumEigensolver()
+        min_eigen_optimizer = MinimumEigenOptimizer(min_eigen_solver)
+        with self.assertWarns(PendingDeprecationWarning):
+            result = min_eigen_optimizer.solve(self.op_maximize)
+        opt_sol = 2
+        self.assertEqual(result.fval, opt_sol)
+        self.assertEqual(len(result.samples), 1)
+        np.testing.assert_array_almost_equal(result.samples[0].x, [0, 1])
+        self.assertAlmostEqual(result.samples[0].fval, opt_sol)
+        self.assertAlmostEqual(result.samples[0].probability, 1.0)
+        self.assertEqual(result.samples[0].status, success)
+        self.assertEqual(len(result.raw_samples), 1)
+        np.testing.assert_array_almost_equal(result.raw_samples[0].x, [0, 0, 1, 0])
+        # optimizer internally deals with minimization problem
+        self.assertAlmostEqual(
+            self.op_maximize.objective.sense.value * result.raw_samples[0].fval, opt_sol
+        )
+        self.assertAlmostEqual(result.raw_samples[0].probability, 1.0)
+        self.assertEqual(result.raw_samples[0].status, success)
+
+    @data("sv", "qasm")
+    def test_samples_qaoa(self, simulator):
+        """Test samples for QAOA"""
+        # test minimize
+        algorithm_globals.random_seed = 4
+        quantum_instance = self.sv_simulator if simulator == "sv" else self.qasm_simulator
+        with self.assertWarns(PendingDeprecationWarning):
+            qaoa = QAOA(optimizer=COBYLA(), quantum_instance=quantum_instance, reps=2)
+        min_eigen_optimizer = MinimumEigenOptimizer(qaoa)
+        with self.assertWarns(PendingDeprecationWarning):
+            result = min_eigen_optimizer.solve(self.op_minimize)
+        success = OptimizationResultStatus.SUCCESS
+        opt_sol = 1
+        self.assertAlmostEqual(sum(s.probability for s in result.samples), 1)
+        self.assertAlmostEqual(sum(s.probability for s in result.raw_samples), 1)
+        self.assertAlmostEqual(min(s.fval for s in result.samples), 0)
+        self.assertAlmostEqual(min(s.fval for s in result.samples if s.status == success), opt_sol)
+        self.assertAlmostEqual(min(s.fval for s in result.raw_samples), opt_sol)
+        for sample in result.raw_samples:
+            self.assertEqual(sample.status, success)
+        np.testing.assert_array_almost_equal(result.x, [1, 0])
+        self.assertAlmostEqual(result.fval, result.samples[0].fval)
+        self.assertEqual(result.status, result.samples[0].status)
+        self.assertAlmostEqual(result.samples[0].fval, opt_sol)
+        self.assertEqual(result.samples[0].status, success)
+        np.testing.assert_array_almost_equal(result.raw_samples[0].x, [1, 0, 0, 0, 0])
+        self.assertAlmostEqual(result.raw_samples[0].fval, opt_sol)
+        self.assertEqual(result.raw_samples[0].status, success)
+        # test maximize
+        opt_sol = 2
+        with self.assertWarns(PendingDeprecationWarning):
+            qaoa = QAOA(optimizer=COBYLA(), quantum_instance=quantum_instance, reps=2)
+        min_eigen_optimizer = MinimumEigenOptimizer(qaoa)
+        with self.assertWarns(PendingDeprecationWarning):
+            result = min_eigen_optimizer.solve(self.op_maximize)
+        self.assertAlmostEqual(sum(s.probability for s in result.samples), 1)
+        self.assertAlmostEqual(sum(s.probability for s in result.raw_samples), 1)
+        self.assertAlmostEqual(max(s.fval for s in result.samples), 5)
+        self.assertAlmostEqual(max(s.fval for s in result.samples if s.status == success), opt_sol)
+        # optimizer internally deals with minimization problem
+        self.assertAlmostEqual(
+            max(self.op_maximize.objective.sense.value * s.fval for s in result.raw_samples),
+            opt_sol,
+        )
+        for sample in result.raw_samples:
+            self.assertEqual(sample.status, success)
+        np.testing.assert_array_almost_equal(result.x, [0, 1])
+        self.assertEqual(result.fval, opt_sol)
+        self.assertEqual(result.status, success)
+        np.testing.assert_array_almost_equal(result.samples[0].x, [0, 1])
+        self.assertAlmostEqual(result.samples[0].fval, opt_sol)
+        self.assertEqual(result.samples[0].status, success)
+        np.testing.assert_array_almost_equal(result.raw_samples[0].x, [0, 0, 1, 0])
+        # optimizer internally deals with minimization problem
+        self.assertAlmostEqual(
+            self.op_maximize.objective.sense.value * result.raw_samples[0].fval, opt_sol
+        )
+        self.assertEqual(result.raw_samples[0].status, success)
+        # test bit ordering
+        opt_sol = -2
+        with self.assertWarns(PendingDeprecationWarning):
+            qaoa = QAOA(optimizer=COBYLA(), quantum_instance=quantum_instance, reps=2)
+        min_eigen_optimizer = MinimumEigenOptimizer(qaoa)
+        with self.assertWarns(PendingDeprecationWarning):
+            result = min_eigen_optimizer.solve(self.op_ordering)
+        self.assertEqual(result.fval, opt_sol)
+        np.testing.assert_array_almost_equal(result.x, [0, 1])
+        self.assertEqual(result.status, success)
+        result.raw_samples.sort(key=lambda x: x.probability, reverse=True)
+        np.testing.assert_array_almost_equal(result.x, result.raw_samples[0].x)
+        self.assertAlmostEqual(sum(s.probability for s in result.samples), 1, delta=1e-5)
+        self.assertAlmostEqual(sum(s.probability for s in result.raw_samples), 1, delta=1e-5)
+        self.assertAlmostEqual(min(s.fval for s in result.samples), -2)
+        self.assertAlmostEqual(min(s.fval for s in result.samples if s.status == success), opt_sol)
+        self.assertAlmostEqual(min(s.fval for s in result.raw_samples), opt_sol)
+        for sample in result.raw_samples:
+            self.assertEqual(sample.status, success)
+        np.testing.assert_array_almost_equal(result.samples[0].x, [0, 1])
+        self.assertAlmostEqual(result.samples[0].fval, opt_sol)
+        self.assertEqual(result.samples[0].status, success)
+        np.testing.assert_array_almost_equal(result.raw_samples[0].x, [0, 1])
+        self.assertAlmostEqual(result.raw_samples[0].fval, opt_sol)
+        self.assertEqual(result.raw_samples[0].status, success)
+
+    @data("sv", "qasm")
+    def test_samples_vqe(self, simulator):
+        """Test samples for VQE"""
+        # test minimize
+        algorithm_globals.random_seed = 1
+        quantum_instance = self.sv_simulator if simulator == "sv" else self.qasm_simulator
+        opt_sol = -2
+        success = OptimizationResultStatus.SUCCESS
+        optimizer = SPSA(maxiter=100)
+        ry_ansatz = TwoLocal(5, "ry", "cz", reps=3, entanglement="full")
+        with self.assertWarns(PendingDeprecationWarning):
+            vqe_mes = VQE(ry_ansatz, optimizer=optimizer, quantum_instance=quantum_instance)
+        vqe = MinimumEigenOptimizer(vqe_mes)
+        with self.assertWarns(PendingDeprecationWarning):
+            results = vqe.solve(self.op_ordering)
+        self.assertEqual(results.fval, opt_sol)
+        np.testing.assert_array_almost_equal(results.x, [0, 1])
+        self.assertEqual(results.status, success)
+        results.raw_samples.sort(key=lambda x: x.probability, reverse=True)
+        np.testing.assert_array_almost_equal(results.x, results.raw_samples[0].x)
+        self.assertAlmostEqual(sum(s.probability for s in results.samples), 1, delta=1e-5)
+        self.assertAlmostEqual(sum(s.probability for s in results.raw_samples), 1, delta=1e-5)
+        self.assertAlmostEqual(min(s.fval for s in results.samples), -2)
+        self.assertAlmostEqual(min(s.fval for s in results.samples if s.status == success), opt_sol)
+        self.assertAlmostEqual(min(s.fval for s in results.raw_samples), opt_sol)
+        for sample in results.raw_samples:
+            self.assertEqual(sample.status, success)
+        np.testing.assert_array_almost_equal(results.samples[0].x, [0, 1])
+        self.assertAlmostEqual(results.samples[0].fval, opt_sol)
+        self.assertEqual(results.samples[0].status, success)
+        np.testing.assert_array_almost_equal(results.raw_samples[0].x, [0, 1])
+        self.assertAlmostEqual(results.raw_samples[0].fval, opt_sol)
+        self.assertEqual(results.raw_samples[0].status, success)
+
+    @data("vqe", "qaoa")
+    def test_runtime(self, subroutine):
+        """Test vqe and qaoa runtime"""
+        optimizer = {"name": "SPSA", "maxiter": 100}
+        backend = QasmSimulatorPy()
+
+        if subroutine == "vqe":
+            ry_ansatz = TwoLocal(5, "ry", "cz", reps=3, entanglement="full")
+            initial_point = np.random.default_rng(42).random(ry_ansatz.num_parameters)
+            solver = VQEClient(
+                ansatz=ry_ansatz,
+                optimizer=optimizer,
+                initial_point=initial_point,
+                backend=backend,
+                provider=FakeVQERuntimeProvider(),
+            )
+        else:
+            reps = 2
+            initial_point = np.random.default_rng(42).random(2 * reps)
+            solver = QAOAClient(
+                optimizer=optimizer,
+                reps=reps,
+                initial_point=initial_point,
+                backend=backend,
+                provider=FakeQAOARuntimeProvider(),
+            )
+
+        opt = MinimumEigenOptimizer(solver)
+        result = opt.solve(self.op_ordering)
+        self.assertIsInstance(result, MinimumEigenOptimizationResult)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/test/algorithms/legacy/test_recursive_optimization.py b/test/algorithms/legacy/test_recursive_optimization.py
new file mode 100644
index 000000000..675f8419a
--- /dev/null
+++ b/test/algorithms/legacy/test_recursive_optimization.py
@@ -0,0 +1,212 @@
+# This code is part of Qiskit.
+#
+# (C) Copyright IBM 2018, 2022.
+#
+# This code is licensed under the Apache License, Version 2.0. You may
+# obtain a copy of this license in the LICENSE.txt file in the root directory
+# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
+#
+# Any modifications or derivative works of this code must retain this
+# copyright notice, and modified files need to carry a notice indicating
+# that they have been altered from the originals.
+
+"""Test Recursive Min Eigen Optimizer with legacy MinimumEigensolver."""
+
+import unittest
+from test import QiskitOptimizationTestCase
+
+import numpy as np
+
+from qiskit import BasicAer
+from qiskit.utils import algorithm_globals, QuantumInstance
+
+from qiskit.algorithms import NumPyMinimumEigensolver, QAOA
+
+import qiskit_optimization.optionals as _optionals
+from qiskit_optimization.algorithms import (
+    MinimumEigenOptimizer,
+    CplexOptimizer,
+    RecursiveMinimumEigenOptimizer,
+    WarmStartQAOAOptimizer,
+    SlsqpOptimizer,
+)
+from qiskit_optimization.algorithms.recursive_minimum_eigen_optimizer import (
+    IntermediateResult,
+)
+from qiskit_optimization.problems import QuadraticProgram
+from qiskit_optimization.converters import (
+    IntegerToBinary,
+    InequalityToEquality,
+    LinearEqualityToPenalty,
+    QuadraticProgramToQubo,
+)
+
+
+class TestRecursiveMinEigenOptimizer(QiskitOptimizationTestCase):
+    """Recursive Min Eigen Optimizer Tests."""
+
+    @unittest.skipIf(not _optionals.HAS_CPLEX, "CPLEX not available.")
+    def test_recursive_min_eigen_optimizer(self):
+        """Test the recursive minimum eigen optimizer."""
+        filename = "op_ip1.lp"
+        # get minimum eigen solver
+        with self.assertWarns(PendingDeprecationWarning):
+            min_eigen_solver = NumPyMinimumEigensolver()
+
+        # construct minimum eigen optimizer
+        min_eigen_optimizer = MinimumEigenOptimizer(min_eigen_solver)
+        recursive_min_eigen_optimizer = RecursiveMinimumEigenOptimizer(
+            min_eigen_optimizer, min_num_vars=4
+        )
+
+        # load optimization problem
+        problem = QuadraticProgram()
+        lp_file = self.get_resource_path(filename, "algorithms/resources")
+        problem.read_from_lp_file(lp_file)
+
+        # solve problem with cplex
+        cplex = CplexOptimizer()
+        cplex_result = cplex.solve(problem)
+
+        # solve problem
+        with self.assertWarns(PendingDeprecationWarning):
+            result = recursive_min_eigen_optimizer.solve(problem)
+
+        # analyze results
+        np.testing.assert_array_almost_equal(cplex_result.x, result.x, 4)
+        self.assertAlmostEqual(cplex_result.fval, result.fval)
+
+    @unittest.skipIf(not _optionals.HAS_CPLEX, "CPLEX not available.")
+    def test_recursive_history(self):
+        """Tests different options for history."""
+        filename = "op_ip1.lp"
+        # load optimization problem
+        problem = QuadraticProgram()
+        lp_file = self.get_resource_path(filename, "algorithms/resources")
+        problem.read_from_lp_file(lp_file)
+
+        # get minimum eigen solver
+        with self.assertWarns(PendingDeprecationWarning):
+            min_eigen_solver = NumPyMinimumEigensolver()
+
+        # construct minimum eigen optimizer
+        min_eigen_optimizer = MinimumEigenOptimizer(min_eigen_solver)
+
+        # no history
+        recursive_min_eigen_optimizer = RecursiveMinimumEigenOptimizer(
+            min_eigen_optimizer,
+            min_num_vars=4,
+            history=IntermediateResult.NO_ITERATIONS,
+        )
+        with self.assertWarns(PendingDeprecationWarning):
+            result = recursive_min_eigen_optimizer.solve(problem)
+        self.assertIsNotNone(result.replacements)
+        self.assertIsNotNone(result.history)
+        self.assertIsNotNone(result.history[0])
+        self.assertEqual(len(result.history[0]), 0)
+        self.assertIsNone(result.history[1])
+
+        # only last iteration in the history
+        recursive_min_eigen_optimizer = RecursiveMinimumEigenOptimizer(
+            min_eigen_optimizer,
+            min_num_vars=4,
+            history=IntermediateResult.LAST_ITERATION,
+        )
+        with self.assertWarns(PendingDeprecationWarning):
+            result = recursive_min_eigen_optimizer.solve(problem)
+        self.assertIsNotNone(result.replacements)
+        self.assertIsNotNone(result.history)
+        self.assertIsNotNone(result.history[0])
+        self.assertEqual(len(result.history[0]), 0)
+        self.assertIsNotNone(result.history[1])
+
+        # full history
+        recursive_min_eigen_optimizer = RecursiveMinimumEigenOptimizer(
+            min_eigen_optimizer,
+            min_num_vars=4,
+            history=IntermediateResult.ALL_ITERATIONS,
+        )
+        with self.assertWarns(PendingDeprecationWarning):
+            result = recursive_min_eigen_optimizer.solve(problem)
+        self.assertIsNotNone(result.replacements)
+        self.assertIsNotNone(result.history)
+        self.assertIsNotNone(result.history[0])
+        self.assertGreater(len(result.history[0]), 1)
+        self.assertIsNotNone(result.history[1])
+
+    @unittest.skipIf(not _optionals.HAS_CPLEX, "CPLEX not available.")
+    def test_recursive_warm_qaoa(self):
+        """Test the recursive optimizer with warm start qaoa."""
+        seed = 1234
+        algorithm_globals.random_seed = seed
+        backend = BasicAer.get_backend("statevector_simulator")
+        with self.assertWarns(PendingDeprecationWarning):
+            qaoa = QAOA(
+                quantum_instance=QuantumInstance(
+                    backend=backend, seed_simulator=seed, seed_transpiler=seed
+                ),
+                reps=1,
+            )
+        warm_qaoa = WarmStartQAOAOptimizer(
+            pre_solver=SlsqpOptimizer(), relax_for_pre_solver=True, qaoa=qaoa
+        )
+
+        recursive_min_eigen_optimizer = RecursiveMinimumEigenOptimizer(warm_qaoa, min_num_vars=4)
+
+        # load optimization problem
+        problem = QuadraticProgram()
+        lp_file = self.get_resource_path("op_ip1.lp", "algorithms/resources")
+        problem.read_from_lp_file(lp_file)
+
+        # solve problem with cplex
+        cplex = CplexOptimizer(cplex_parameters={"threads": 1, "randomseed": 1})
+        cplex_result = cplex.solve(problem)
+
+        # solve problem
+        with self.assertWarns(PendingDeprecationWarning):
+            result = recursive_min_eigen_optimizer.solve(problem)
+
+        # analyze results
+        np.testing.assert_array_almost_equal(cplex_result.x, result.x, 4)
+        self.assertAlmostEqual(cplex_result.fval, result.fval)
+
+    def test_converter_list(self):
+        """Test converter list"""
+        op = QuadraticProgram()
+        op.integer_var(0, 3, "x")
+        op.binary_var("y")
+
+        op.maximize(linear={"x": 1, "y": 2})
+        op.linear_constraint(linear={"y": 1, "x": 1}, sense="LE", rhs=3, name="xy_leq")
+
+        # construct minimum eigen optimizer
+        with self.assertWarns(PendingDeprecationWarning):
+            min_eigen_solver = NumPyMinimumEigensolver()
+        min_eigen_optimizer = MinimumEigenOptimizer(min_eigen_solver)
+        # a single converter
+        qp2qubo = QuadraticProgramToQubo()
+        recursive_min_eigen_optimizer = RecursiveMinimumEigenOptimizer(
+            min_eigen_optimizer, min_num_vars=2, converters=qp2qubo
+        )
+        with self.assertWarns(PendingDeprecationWarning):
+            result = recursive_min_eigen_optimizer.solve(op)
+        self.assertEqual(result.fval, 4)
+        # a list of converters
+        ineq2eq = InequalityToEquality()
+        int2bin = IntegerToBinary()
+        penalize = LinearEqualityToPenalty()
+        converters = [ineq2eq, int2bin, penalize]
+        recursive_min_eigen_optimizer = RecursiveMinimumEigenOptimizer(
+            min_eigen_optimizer, min_num_vars=2, converters=converters
+        )
+        with self.assertWarns(PendingDeprecationWarning):
+            result = recursive_min_eigen_optimizer.solve(op)
+        self.assertEqual(result.fval, 4)
+        # invalid converters
+        with self.assertRaises(TypeError):
+            invalid = [qp2qubo, "invalid converter"]
+            RecursiveMinimumEigenOptimizer(min_eigen_optimizer, min_num_vars=2, converters=invalid)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/test/algorithms/legacy/test_warm_start_qaoa.py b/test/algorithms/legacy/test_warm_start_qaoa.py
new file mode 100644
index 000000000..3893f0e23
--- /dev/null
+++ b/test/algorithms/legacy/test_warm_start_qaoa.py
@@ -0,0 +1,140 @@
+# This code is part of Qiskit.
+#
+# (C) Copyright IBM 2021, 2022.
+#
+# This code is licensed under the Apache License, Version 2.0. You may
+# obtain a copy of this license in the LICENSE.txt file in the root directory
+# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
+#
+# Any modifications or derivative works of this code must retain this
+# copyright notice, and modified files need to carry a notice indicating
+# that they have been altered from the originals.
+
+""" Test warm start QAOA optimizer with legacy QAOA. """
+
+import unittest
+from test import QiskitOptimizationTestCase
+
+import numpy as np
+
+from docplex.mp.model import Model
+from qiskit import BasicAer
+from qiskit.algorithms import QAOA
+
+import qiskit_optimization.optionals as _optionals
+from qiskit_optimization.algorithms import SlsqpOptimizer
+from qiskit_optimization.algorithms.goemans_williamson_optimizer import (
+    GoemansWilliamsonOptimizer,
+)
+from qiskit_optimization.algorithms.warm_start_qaoa_optimizer import (
+    MeanAggregator,
+    WarmStartQAOAOptimizer,
+)
+from qiskit_optimization.applications.max_cut import Maxcut
+from qiskit_optimization.translators import from_docplex_mp
+
+
+class TestWarmStartQAOAOptimizer(QiskitOptimizationTestCase):
+    """Tests for the warm start QAOA optimizer."""
+
+    @unittest.skipIf(not _optionals.HAS_CVXPY, "CVXPY not available.")
+    def test_max_cut(self):
+        """Basic test on the max cut problem."""
+        graph = np.array(
+            [
+                [0.0, 1.0, 2.0, 0.0],
+                [1.0, 0.0, 1.0, 0.0],
+                [2.0, 1.0, 0.0, 1.0],
+                [0.0, 0.0, 1.0, 0.0],
+            ]
+        )
+
+        presolver = GoemansWilliamsonOptimizer(num_cuts=10)
+        problem = Maxcut(graph).to_quadratic_program()
+
+        backend = BasicAer.get_backend("statevector_simulator")
+        with self.assertWarns(PendingDeprecationWarning):
+            qaoa = QAOA(quantum_instance=backend, reps=1)
+        aggregator = MeanAggregator()
+        optimizer = WarmStartQAOAOptimizer(
+            pre_solver=presolver,
+            relax_for_pre_solver=False,
+            qaoa=qaoa,
+            epsilon=0.25,
+            num_initial_solutions=10,
+            aggregator=aggregator,
+        )
+        with self.assertWarns(PendingDeprecationWarning):
+            result_warm = optimizer.solve(problem)
+
+        self.assertIsNotNone(result_warm)
+        self.assertIsNotNone(result_warm.x)
+        np.testing.assert_almost_equal([0, 0, 1, 0], result_warm.x, 3)
+        self.assertIsNotNone(result_warm.fval)
+        np.testing.assert_almost_equal(4, result_warm.fval, 3)
+
+    def test_constrained_binary(self):
+        """Constrained binary optimization problem."""
+        model = Model()
+        v = model.binary_var(name="v")
+        w = model.binary_var(name="w")
+        # pylint:disable=invalid-name
+        t = model.binary_var(name="t")
+
+        model.minimize(v + w + t)
+        model.add_constraint(2 * v + 10 * w + t <= 3, "cons1")
+        model.add_constraint(v + w + t >= 2, "cons2")
+
+        problem = from_docplex_mp(model)
+
+        backend = BasicAer.get_backend("statevector_simulator")
+        with self.assertWarns(PendingDeprecationWarning):
+            qaoa = QAOA(quantum_instance=backend, reps=1)
+        aggregator = MeanAggregator()
+        optimizer = WarmStartQAOAOptimizer(
+            pre_solver=SlsqpOptimizer(),
+            relax_for_pre_solver=True,
+            qaoa=qaoa,
+            epsilon=0.25,
+            aggregator=aggregator,
+        )
+        with self.assertWarns(PendingDeprecationWarning):
+            result_warm = optimizer.solve(problem)
+
+        self.assertIsNotNone(result_warm)
+        self.assertIsNotNone(result_warm.x)
+        np.testing.assert_almost_equal([1, 0, 1], result_warm.x, 3)
+        self.assertIsNotNone(result_warm.fval)
+        np.testing.assert_almost_equal(2, result_warm.fval, 3)
+
+    def test_simple_qubo(self):
+        """Test on a simple QUBO problem."""
+        model = Model()
+        # pylint:disable=invalid-name
+        u = model.binary_var(name="u")
+        v = model.binary_var(name="v")
+
+        model.minimize((u - v + 2) ** 2)
+        problem = from_docplex_mp(model)
+
+        backend = BasicAer.get_backend("statevector_simulator")
+        with self.assertWarns(PendingDeprecationWarning):
+            qaoa = QAOA(quantum_instance=backend, reps=1)
+        optimizer = WarmStartQAOAOptimizer(
+            pre_solver=SlsqpOptimizer(),
+            relax_for_pre_solver=True,
+            qaoa=qaoa,
+            epsilon=0.25,
+        )
+        with self.assertWarns(PendingDeprecationWarning):
+            result_warm = optimizer.solve(problem)
+
+        self.assertIsNotNone(result_warm)
+        self.assertIsNotNone(result_warm.x)
+        np.testing.assert_almost_equal([0, 1], result_warm.x, 3)
+        self.assertIsNotNone(result_warm.fval)
+        np.testing.assert_almost_equal(1, result_warm.fval, 3)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/test/algorithms/test_grover_optimizer.py b/test/algorithms/test_grover_optimizer.py
index a029590fa..173addefa 100644
--- a/test/algorithms/test_grover_optimizer.py
+++ b/test/algorithms/test_grover_optimizer.py
@@ -19,7 +19,7 @@
 from ddt import data, ddt
 from docplex.mp.model import Model
 from qiskit.utils import QuantumInstance, algorithm_globals, optionals
-from qiskit.algorithms import NumPyMinimumEigensolver
+from qiskit.algorithms.minimum_eigensolvers import NumPyMinimumEigensolver
 from qiskit_optimization.algorithms import (
     GroverOptimizer,
     MinimumEigenOptimizer,
@@ -45,6 +45,7 @@ def setUp(self):
         super().setUp()
         algorithm_globals.random_seed = 1
         from qiskit_aer import Aer
+        from qiskit_aer.primitives import Sampler
 
         self.sv_simulator = QuantumInstance(
             Aer.get_backend("aer_simulator_statevector"),
@@ -56,8 +57,38 @@ def setUp(self):
             seed_simulator=123,
             seed_transpiler=123,
         )
+        self.sampler = Sampler(run_options={"seed_simulator": 123})
         self.n_iter = 8
 
+    def _prepare_grover_optimizer(
+        self, num_value_qubits, num_iterations, simulator, converters=None
+    ):
+        """Prepare GroverOptimizer."""
+        if simulator == "statevector":
+            with self.assertWarns(PendingDeprecationWarning):
+                grover_optimizer = GroverOptimizer(
+                    num_value_qubits=num_value_qubits,
+                    num_iterations=num_iterations,
+                    converters=converters,
+                    quantum_instance=self.sv_simulator,
+                )
+        elif simulator == "qasm":
+            with self.assertWarns(PendingDeprecationWarning):
+                grover_optimizer = GroverOptimizer(
+                    num_value_qubits=num_value_qubits,
+                    num_iterations=num_iterations,
+                    converters=converters,
+                    quantum_instance=self.qasm_simulator,
+                )
+        else:
+            grover_optimizer = GroverOptimizer(
+                num_value_qubits=num_value_qubits,
+                num_iterations=num_iterations,
+                converters=converters,
+                sampler=self.sampler,
+            )
+        return grover_optimizer
+
     def validate_results(self, problem, results):
         """Validate the results object returned by GroverOptimizer."""
         # Get expected value.
@@ -71,7 +102,8 @@ def validate_results(self, problem, results):
             results.fval, problem.objective.sense.value * results.intermediate_fval
         )
 
-    def test_qubo_gas_int_zero(self):
+    @data("statevector", "qasm", "sampler")
+    def test_qubo_gas_int_zero(self, simulator):
         """Test for when the answer is zero."""
 
         # Input.
@@ -82,13 +114,16 @@ def test_qubo_gas_int_zero(self):
         op = from_docplex_mp(model)
 
         # Will not find a negative, should return 0.
-        gmf = GroverOptimizer(1, num_iterations=1, quantum_instance=self.sv_simulator)
-        results = gmf.solve(op)
+        grover_optimizer = self._prepare_grover_optimizer(
+            num_value_qubits=1, num_iterations=1, simulator=simulator
+        )
+        results = grover_optimizer.solve(op)
         np.testing.assert_array_almost_equal(results.x, [0, 0])
         self.assertEqual(results.fval, 0.0)
         self.assertAlmostEqual(results.fval, results.intermediate_fval)
 
-    def test_qubo_gas_int_simple(self):
+    @data("statevector", "qasm", "sampler")
+    def test_qubo_gas_int_simple(self, simulator):
         """Test for simple case, with 2 linear coeffs and no quadratic coeffs or constants."""
 
         # Input.
@@ -99,15 +134,18 @@ def test_qubo_gas_int_simple(self):
         op = from_docplex_mp(model)
 
         # Get the optimum key and value.
-        gmf = GroverOptimizer(4, num_iterations=self.n_iter, quantum_instance=self.sv_simulator)
-        results = gmf.solve(op)
+        grover_optimizer = self._prepare_grover_optimizer(
+            num_value_qubits=4, num_iterations=self.n_iter, simulator=simulator
+        )
+        results = grover_optimizer.solve(op)
         self.validate_results(op, results)
 
         self.assertIsNotNone(results.operation_counts)
         self.assertEqual(results.n_input_qubits, 2)
         self.assertEqual(results.n_output_qubits, 4)
 
-    def test_qubo_gas_int_simple_maximize(self):
+    @data("statevector", "qasm", "sampler")
+    def test_qubo_gas_int_simple_maximize(self, simulator):
         """Test for simple case, but with maximization."""
 
         # Input.
@@ -118,11 +156,13 @@ def test_qubo_gas_int_simple_maximize(self):
         op = from_docplex_mp(model)
 
         # Get the optimum key and value.
-        gmf = GroverOptimizer(4, num_iterations=self.n_iter, quantum_instance=self.sv_simulator)
-        results = gmf.solve(op)
+        grover_optimizer = self._prepare_grover_optimizer(
+            num_value_qubits=4, num_iterations=self.n_iter, simulator=simulator
+        )
+        results = grover_optimizer.solve(op)
         self.validate_results(op, results)
 
-    @data("sv", "qasm")
+    @data("statevector", "qasm", "sampler")
     def test_qubo_gas_int_paper_example(self, simulator):
         """
         Test the example from https://arxiv.org/abs/1912.04088 using the state vector simulator
@@ -138,12 +178,14 @@ def test_qubo_gas_int_paper_example(self, simulator):
         op = from_docplex_mp(model)
 
         # Get the optimum key and value.
-        q_instance = self.sv_simulator if simulator == "sv" else self.qasm_simulator
-        gmf = GroverOptimizer(6, num_iterations=self.n_iter, quantum_instance=q_instance)
-        results = gmf.solve(op)
+        grover_optimizer = self._prepare_grover_optimizer(
+            num_value_qubits=6, num_iterations=self.n_iter, simulator=simulator
+        )
+        results = grover_optimizer.solve(op)
         self.validate_results(op, results)
 
-    def test_converter_list(self):
+    @data("statevector", "qasm", "sampler")
+    def test_converter_list(self, simulator):
         """Test converters list"""
         # Input.
 
@@ -156,13 +198,10 @@ def test_converter_list(self):
         # Get the optimum key and value.
         # a single converter.
         qp2qubo = QuadraticProgramToQubo()
-        gmf = GroverOptimizer(
-            4,
-            num_iterations=self.n_iter,
-            quantum_instance=self.sv_simulator,
-            converters=qp2qubo,
+        grover_optimizer = self._prepare_grover_optimizer(
+            num_value_qubits=4, num_iterations=self.n_iter, simulator=simulator
         )
-        results = gmf.solve(op)
+        results = grover_optimizer.solve(op)
         self.validate_results(op, results)
 
         # a list of converters
@@ -171,25 +210,22 @@ def test_converter_list(self):
         penalize = LinearEqualityToPenalty()
         max2min = MaximizeToMinimize()
         converters = [ineq2eq, int2bin, penalize, max2min]
-        gmf = GroverOptimizer(
-            4,
+        grover_optimizer = self._prepare_grover_optimizer(
+            num_value_qubits=4,
             num_iterations=self.n_iter,
-            quantum_instance=self.sv_simulator,
+            simulator=simulator,
             converters=converters,
         )
-        results = gmf.solve(op)
+        results = grover_optimizer.solve(op)
         self.validate_results(op, results)
         # invalid converters
         with self.assertRaises(TypeError):
             invalid = [qp2qubo, "invalid converter"]
-            GroverOptimizer(
-                4,
-                num_iterations=self.n_iter,
-                quantum_instance=self.sv_simulator,
-                converters=invalid,
+            grover_optimizer = self._prepare_grover_optimizer(
+                4, num_iterations=self.n_iter, simulator=simulator, converters=invalid
             )
 
-    @data("sv", "qasm")
+    @data("statevector", "qasm", "sampler")
     def test_samples_and_raw_samples(self, simulator):
         """Test samples and raw_samples"""
         algorithm_globals.random_seed = 2
@@ -198,9 +234,8 @@ def test_samples_and_raw_samples(self, simulator):
         op.binary_var("y")
         op.minimize(linear={"x": 1, "y": 2})
         op.linear_constraint(linear={"x": 1, "y": 1}, sense=">=", rhs=1, name="xy")
-        q_instance = self.sv_simulator if simulator == "sv" else self.qasm_simulator
-        grover_optimizer = GroverOptimizer(
-            8, num_iterations=self.n_iter, quantum_instance=q_instance
+        grover_optimizer = self._prepare_grover_optimizer(
+            num_value_qubits=8, num_iterations=self.n_iter, simulator=simulator
         )
         opt_sol = 1
         success = OptimizationResultStatus.SUCCESS
@@ -221,12 +256,11 @@ def test_samples_and_raw_samples(self, simulator):
         self.assertEqual(results.status, results.raw_samples[0].status)
         np.testing.assert_array_almost_equal([1, 0, 0, 0, 0], results.raw_samples[0].x)
 
-    @data("sv", "qasm")
+    @data("statevector", "qasm", "sampler")
     def test_bit_ordering(self, simulator):
         """Test bit ordering"""
         # test minimize
         algorithm_globals.random_seed = 2
-        q_instance = self.sv_simulator if simulator == "sv" else self.qasm_simulator
         mdl = Model("docplex model")
         x = mdl.binary_var("x")
         y = mdl.binary_var("y")
@@ -234,8 +268,8 @@ def test_bit_ordering(self, simulator):
         op = from_docplex_mp(mdl)
         opt_sol = -2
         success = OptimizationResultStatus.SUCCESS
-        grover_optimizer = GroverOptimizer(
-            3, num_iterations=self.n_iter, quantum_instance=q_instance
+        grover_optimizer = self._prepare_grover_optimizer(
+            num_value_qubits=3, num_iterations=self.n_iter, simulator=simulator
         )
         results = grover_optimizer.solve(op)
         self.assertEqual(results.fval, opt_sol)
diff --git a/test/algorithms/test_min_eigen_optimizer.py b/test/algorithms/test_min_eigen_optimizer.py
index 257fe7fe4..e404b2579 100644
--- a/test/algorithms/test_min_eigen_optimizer.py
+++ b/test/algorithms/test_min_eigen_optimizer.py
@@ -1,6 +1,6 @@
 # This code is part of Qiskit.
 #
-# (C) Copyright IBM 2018, 2022.
+# (C) Copyright IBM 2018, 2023.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -10,30 +10,29 @@
 # copyright notice, and modified files need to carry a notice indicating
 # that they have been altered from the originals.
 
-""" Test Min Eigen Optimizer """
+""" Test Min Eigen Optimizer with the primitive-based minimum eigensolver """
 
 import unittest
 from test.optimization_test_case import QiskitOptimizationTestCase
-
-from test.runtime.fake_vqeruntime import FakeVQERuntimeProvider, FakeQAOARuntimeProvider
+from test.runtime.fake_vqeruntime import FakeQAOARuntimeProvider, FakeVQERuntimeProvider
 
 import numpy as np
-from ddt import data, ddt
-from qiskit import BasicAer
-from qiskit.algorithms import QAOA, VQE, NumPyMinimumEigensolver
+from ddt import data, ddt, unpack
+from qiskit.algorithms.minimum_eigensolvers import QAOA, NumPyMinimumEigensolver, SamplingVQE, VQE
 from qiskit.algorithms.optimizers import COBYLA, SPSA
 from qiskit.circuit.library import TwoLocal
+from qiskit.primitives import Estimator, Sampler
 from qiskit.providers.basicaer import QasmSimulatorPy
-from qiskit.utils import QuantumInstance, algorithm_globals
+from qiskit.providers.fake_provider import FakeArmonk, FakeArmonkV2
+from qiskit.utils import algorithm_globals
+
 import qiskit_optimization.optionals as _optionals
 from qiskit_optimization.algorithms import (
     CplexOptimizer,
-    MinimumEigenOptimizer,
     MinimumEigenOptimizationResult,
+    MinimumEigenOptimizer,
 )
-from qiskit_optimization.algorithms.optimization_algorithm import (
-    OptimizationResultStatus,
-)
+from qiskit_optimization.algorithms.optimization_algorithm import OptimizationResultStatus
 from qiskit_optimization.converters import (
     InequalityToEquality,
     IntegerToBinary,
@@ -42,7 +41,7 @@
     QuadraticProgramToQubo,
 )
 from qiskit_optimization.problems import QuadraticProgram
-from qiskit_optimization.runtime import VQEProgram, QAOAProgram
+from qiskit_optimization.runtime import QAOAClient, VQEClient
 
 
 @ddt
@@ -52,26 +51,14 @@ class TestMinEigenOptimizer(QiskitOptimizationTestCase):
     def setUp(self):
         super().setUp()
 
+        self._seed = 123
+
         # setup minimum eigen solvers
-        self.min_eigen_solvers = {}
-
-        # exact eigen solver
-        self.min_eigen_solvers["exact"] = NumPyMinimumEigensolver()
-
-        # QAOA
-        optimizer = COBYLA()
-        self.min_eigen_solvers["qaoa"] = QAOA(optimizer=optimizer)
-        # simulators
-        self.sv_simulator = QuantumInstance(
-            BasicAer.get_backend("statevector_simulator"),
-            seed_simulator=123,
-            seed_transpiler=123,
-        )
-        self.qasm_simulator = QuantumInstance(
-            BasicAer.get_backend("qasm_simulator"),
-            seed_simulator=123,
-            seed_transpiler=123,
-        )
+        self.min_eigen_solvers = {
+            "exact": NumPyMinimumEigensolver(),
+            "qaoa": QAOA(sampler=Sampler(), optimizer=COBYLA()),
+        }
+
         # test minimize
         self.op_minimize = QuadraticProgram()
         self.op_minimize.integer_var(0, 3, "x")
@@ -94,20 +81,19 @@ def setUp(self):
 
     @data(
         ("exact", None, "op_ip1.lp"),
-        ("qaoa", "statevector_simulator", "op_ip1.lp"),
-        ("qaoa", "qasm_simulator", "op_ip1.lp"),
+        ("qaoa", None, "op_ip1.lp"),
+        ("qaoa", 10000, "op_ip1.lp"),
     )
+    @unpack
     @unittest.skipIf(not _optionals.HAS_CPLEX, "CPLEX not available.")
-    def test_min_eigen_optimizer(self, config):
+    def test_min_eigen_optimizer(self, min_eigen_solver_name, shots, filename):
         """Min Eigen Optimizer Test"""
         try:
-            # unpack configuration
-            min_eigen_solver_name, backend, filename = config
-
             # get minimum eigen solver
             min_eigen_solver = self.min_eigen_solvers[min_eigen_solver_name]
-            if backend:
-                min_eigen_solver.quantum_instance = BasicAer.get_backend(backend)
+            if min_eigen_solver_name == "qaoa":
+                min_eigen_solver.sampler.options.shots = shots
+                min_eigen_solver.sampler.options.seed = self._seed
 
             # construct minimum eigen optimizer
             min_eigen_optimizer = MinimumEigenOptimizer(min_eigen_solver)
@@ -137,13 +123,11 @@ def test_min_eigen_optimizer(self, config):
         ("op_ip1.lp", -470, 12, OptimizationResultStatus.SUCCESS),
         ("op_ip1.lp", np.inf, None, OptimizationResultStatus.FAILURE),
     )
+    @unpack
     @unittest.skipIf(not _optionals.HAS_CPLEX, "CPLEX not available.")
-    def test_min_eigen_optimizer_with_filter(self, config):
+    def test_min_eigen_optimizer_with_filter(self, filename, lowerbound, fval, status):
         """Min Eigen Optimizer Test"""
         try:
-            # unpack configuration
-            filename, lowerbound, fval, status = config
-
             # get minimum eigen solver
             min_eigen_solver = NumPyMinimumEigensolver()
 
@@ -241,13 +225,12 @@ def test_samples_numpy_eigen_solver(self):
         self.assertAlmostEqual(result.raw_samples[0].probability, 1.0)
         self.assertEqual(result.raw_samples[0].status, success)
 
-    @data("sv", "qasm")
-    def test_samples_qaoa(self, simulator):
+    def test_samples_qaoa(self):
         """Test samples for QAOA"""
         # test minimize
         algorithm_globals.random_seed = 4
-        quantum_instance = self.sv_simulator if simulator == "sv" else self.qasm_simulator
-        qaoa = QAOA(optimizer=COBYLA(), quantum_instance=quantum_instance, reps=2)
+        sampler = Sampler()
+        qaoa = QAOA(sampler=sampler, optimizer=COBYLA(), reps=2)
         min_eigen_optimizer = MinimumEigenOptimizer(qaoa)
         result = min_eigen_optimizer.solve(self.op_minimize)
         success = OptimizationResultStatus.SUCCESS
@@ -269,7 +252,7 @@ def test_samples_qaoa(self, simulator):
         self.assertEqual(result.raw_samples[0].status, success)
         # test maximize
         opt_sol = 2
-        qaoa = QAOA(optimizer=COBYLA(), quantum_instance=quantum_instance, reps=2)
+        qaoa = QAOA(sampler=sampler, optimizer=COBYLA(), reps=2)
         min_eigen_optimizer = MinimumEigenOptimizer(qaoa)
         result = min_eigen_optimizer.solve(self.op_maximize)
         self.assertAlmostEqual(sum(s.probability for s in result.samples), 1)
@@ -297,7 +280,7 @@ def test_samples_qaoa(self, simulator):
         self.assertEqual(result.raw_samples[0].status, success)
         # test bit ordering
         opt_sol = -2
-        qaoa = QAOA(optimizer=COBYLA(), quantum_instance=quantum_instance, reps=2)
+        qaoa = QAOA(sampler=sampler, optimizer=COBYLA(), reps=2)
         min_eigen_optimizer = MinimumEigenOptimizer(qaoa)
         result = min_eigen_optimizer.solve(self.op_ordering)
         self.assertEqual(result.fval, opt_sol)
@@ -319,17 +302,16 @@ def test_samples_qaoa(self, simulator):
         self.assertAlmostEqual(result.raw_samples[0].fval, opt_sol)
         self.assertEqual(result.raw_samples[0].status, success)
 
-    @data("sv", "qasm")
-    def test_samples_vqe(self, simulator):
+    def test_samples_vqe(self):
         """Test samples for VQE"""
         # test minimize
         algorithm_globals.random_seed = 1
-        quantum_instance = self.sv_simulator if simulator == "sv" else self.qasm_simulator
         opt_sol = -2
         success = OptimizationResultStatus.SUCCESS
         optimizer = SPSA(maxiter=100)
         ry_ansatz = TwoLocal(5, "ry", "cz", reps=3, entanglement="full")
-        vqe_mes = VQE(ry_ansatz, optimizer=optimizer, quantum_instance=quantum_instance)
+        sampler = Sampler()
+        vqe_mes = SamplingVQE(sampler, ry_ansatz, optimizer=optimizer)
         vqe = MinimumEigenOptimizer(vqe_mes)
         results = vqe.solve(self.op_ordering)
         self.assertEqual(results.fval, opt_sol)
@@ -351,6 +333,15 @@ def test_samples_vqe(self, simulator):
         self.assertAlmostEqual(results.raw_samples[0].fval, opt_sol)
         self.assertEqual(results.raw_samples[0].status, success)
 
+    def test_errors(self):
+        """Test for errors"""
+        optimizer = SPSA(maxiter=100)
+        ry_ansatz = TwoLocal(5, "ry", "cz", reps=3, entanglement="full")
+        estimator = Estimator()
+        vqe = VQE(estimator, ry_ansatz, optimizer)
+        with self.assertRaises(TypeError):
+            _ = MinimumEigenOptimizer(vqe)
+
     @data("vqe", "qaoa")
     def test_runtime(self, subroutine):
         """Test vqe and qaoa runtime"""
@@ -360,7 +351,7 @@ def test_runtime(self, subroutine):
         if subroutine == "vqe":
             ry_ansatz = TwoLocal(5, "ry", "cz", reps=3, entanglement="full")
             initial_point = np.random.default_rng(42).random(ry_ansatz.num_parameters)
-            solver = VQEProgram(
+            solver = VQEClient(
                 ansatz=ry_ansatz,
                 optimizer=optimizer,
                 initial_point=initial_point,
@@ -370,7 +361,7 @@ def test_runtime(self, subroutine):
         else:
             reps = 2
             initial_point = np.random.default_rng(42).random(2 * reps)
-            solver = QAOAProgram(
+            solver = QAOAClient(
                 optimizer=optimizer,
                 reps=reps,
                 initial_point=initial_point,
@@ -382,6 +373,20 @@ def test_runtime(self, subroutine):
         result = opt.solve(self.op_ordering)
         self.assertIsInstance(result, MinimumEigenOptimizationResult)
 
+    @data(FakeArmonk, FakeArmonkV2)
+    def test_runtime_backend_versions(self, backend_cls):
+        """Test the runtime client with a V1 and a V2 backend."""
+        optimizer = SPSA(maxiter=1, learning_rate=0.1, perturbation=0.1)
+        backend = backend_cls()
+        provider = FakeVQERuntimeProvider()
+        ansatz = TwoLocal(1, "ry", reps=0)
+        initial_point = np.array([1])
+
+        solver = VQEClient(ansatz, optimizer, initial_point, provider, backend)
+        opt = MinimumEigenOptimizer(solver)
+        result = opt.solve(self.op_ordering)
+        self.assertIsInstance(result, MinimumEigenOptimizationResult)
+
 
 if __name__ == "__main__":
     unittest.main()
diff --git a/test/algorithms/test_recursive_optimization.py b/test/algorithms/test_recursive_optimization.py
old mode 100755
new mode 100644
index 53e206b7b..ec8c6d9cc
--- a/test/algorithms/test_recursive_optimization.py
+++ b/test/algorithms/test_recursive_optimization.py
@@ -10,36 +10,33 @@
 # copyright notice, and modified files need to carry a notice indicating
 # that they have been altered from the originals.
 
-"""Test Recursive Min Eigen Optimizer."""
+"""Test Recursive Min Eigen Optimizer with the primitive-based minimum eigensolver."""
 
 import unittest
 from test import QiskitOptimizationTestCase
 
 import numpy as np
-
-from qiskit import BasicAer
-from qiskit.utils import algorithm_globals, QuantumInstance
-
-from qiskit.algorithms import NumPyMinimumEigensolver, QAOA
+from qiskit.algorithms.minimum_eigensolvers import QAOA, NumPyMinimumEigensolver
+from qiskit.algorithms.optimizers import SLSQP
+from qiskit.primitives import Sampler
+from qiskit.utils import algorithm_globals
 
 import qiskit_optimization.optionals as _optionals
 from qiskit_optimization.algorithms import (
-    MinimumEigenOptimizer,
     CplexOptimizer,
+    MinimumEigenOptimizer,
     RecursiveMinimumEigenOptimizer,
-    WarmStartQAOAOptimizer,
     SlsqpOptimizer,
+    WarmStartQAOAOptimizer,
 )
-from qiskit_optimization.algorithms.recursive_minimum_eigen_optimizer import (
-    IntermediateResult,
-)
-from qiskit_optimization.problems import QuadraticProgram
+from qiskit_optimization.algorithms.recursive_minimum_eigen_optimizer import IntermediateResult
 from qiskit_optimization.converters import (
-    IntegerToBinary,
     InequalityToEquality,
+    IntegerToBinary,
     LinearEqualityToPenalty,
     QuadraticProgramToQubo,
 )
+from qiskit_optimization.problems import QuadraticProgram
 
 
 class TestRecursiveMinEigenOptimizer(QiskitOptimizationTestCase):
@@ -133,11 +130,9 @@ def test_recursive_warm_qaoa(self):
         """Test the recursive optimizer with warm start qaoa."""
         seed = 1234
         algorithm_globals.random_seed = seed
-        backend = BasicAer.get_backend("statevector_simulator")
         qaoa = QAOA(
-            quantum_instance=QuantumInstance(
-                backend=backend, seed_simulator=seed, seed_transpiler=seed
-            ),
+            sampler=Sampler(),
+            optimizer=SLSQP(),
             reps=1,
         )
         warm_qaoa = WarmStartQAOAOptimizer(
diff --git a/test/algorithms/test_warm_start_qaoa.py b/test/algorithms/test_warm_start_qaoa.py
index 39f0dba0c..be83a392b 100644
--- a/test/algorithms/test_warm_start_qaoa.py
+++ b/test/algorithms/test_warm_start_qaoa.py
@@ -10,22 +10,20 @@
 # copyright notice, and modified files need to carry a notice indicating
 # that they have been altered from the originals.
 
-""" Test warm start QAOA optimizer. """
+""" Test warm start QAOA optimizer with the primitive-based minimum eigensolver. """
 
 import unittest
 from test import QiskitOptimizationTestCase
 
 import numpy as np
-
 from docplex.mp.model import Model
-from qiskit import BasicAer
-from qiskit.algorithms import QAOA
+from qiskit.algorithms.minimum_eigensolvers import QAOA
+from qiskit.algorithms.optimizers import SLSQP
+from qiskit.primitives.sampler import Sampler
 
 import qiskit_optimization.optionals as _optionals
 from qiskit_optimization.algorithms import SlsqpOptimizer
-from qiskit_optimization.algorithms.goemans_williamson_optimizer import (
-    GoemansWilliamsonOptimizer,
-)
+from qiskit_optimization.algorithms.goemans_williamson_optimizer import GoemansWilliamsonOptimizer
 from qiskit_optimization.algorithms.warm_start_qaoa_optimizer import (
     MeanAggregator,
     WarmStartQAOAOptimizer,
@@ -52,8 +50,7 @@ def test_max_cut(self):
         presolver = GoemansWilliamsonOptimizer(num_cuts=10)
         problem = Maxcut(graph).to_quadratic_program()
 
-        backend = BasicAer.get_backend("statevector_simulator")
-        qaoa = QAOA(quantum_instance=backend, reps=1)
+        qaoa = QAOA(sampler=Sampler(), optimizer=SLSQP(), reps=1)
         aggregator = MeanAggregator()
         optimizer = WarmStartQAOAOptimizer(
             pre_solver=presolver,
@@ -85,8 +82,7 @@ def test_constrained_binary(self):
 
         problem = from_docplex_mp(model)
 
-        backend = BasicAer.get_backend("statevector_simulator")
-        qaoa = QAOA(quantum_instance=backend, reps=1)
+        qaoa = QAOA(sampler=Sampler(), optimizer=SLSQP(), reps=1)
         aggregator = MeanAggregator()
         optimizer = WarmStartQAOAOptimizer(
             pre_solver=SlsqpOptimizer(),
@@ -113,8 +109,7 @@ def test_simple_qubo(self):
         model.minimize((u - v + 2) ** 2)
         problem = from_docplex_mp(model)
 
-        backend = BasicAer.get_backend("statevector_simulator")
-        qaoa = QAOA(quantum_instance=backend, reps=1)
+        qaoa = QAOA(sampler=Sampler(), optimizer=SLSQP(), reps=1)
         optimizer = WarmStartQAOAOptimizer(
             pre_solver=SlsqpOptimizer(),
             relax_for_pre_solver=True,
diff --git a/test/applications/test_optimization_application.py b/test/applications/test_optimization_application.py
new file mode 100644
index 000000000..7c5e5f020
--- /dev/null
+++ b/test/applications/test_optimization_application.py
@@ -0,0 +1,44 @@
+# This code is part of Qiskit.
+#
+# (C) Copyright IBM 2022.
+#
+# This code is licensed under the Apache License, Version 2.0. You may
+# obtain a copy of this license in the LICENSE.txt file in the root directory
+# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
+#
+# Any modifications or derivative works of this code must retain this
+# copyright notice, and modified files need to carry a notice indicating
+# that they have been altered from the originals.
+
+"""Test OptimizationApplication class"""
+
+import unittest
+from test.optimization_test_case import QiskitOptimizationTestCase
+
+import numpy as np
+from ddt import data, ddt
+from qiskit.opflow import StateFn
+from qiskit.result import QuasiDistribution
+
+from qiskit_optimization.applications import OptimizationApplication
+
+
+@ddt
+class TestOptimizationApplication(QiskitOptimizationTestCase):
+    """Test OptimizationApplication class"""
+
+    @data(
+        np.array([0, 0, 1, 0]),
+        StateFn([0, 0, 1, 0]),
+        {"10": 0.8, "01": 0.2},
+        QuasiDistribution({"10": 0.8, "01": 0.2}),
+    )
+    def test_sample_most_likely(self, state_vector):
+        """Test sample_most_likely"""
+
+        result = OptimizationApplication.sample_most_likely(state_vector)
+        np.testing.assert_allclose(result, [0, 1])
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/test/applications/test_sk_model.py b/test/applications/test_sk_model.py
index d4027f091..a6d90e52e 100644
--- a/test/applications/test_sk_model.py
+++ b/test/applications/test_sk_model.py
@@ -1,6 +1,6 @@
 # This code is part of Qiskit.
 #
-# (C) Copyright IBM 2021.
+# (C) Copyright IBM 2021, 2022.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -29,8 +29,8 @@ def setUp(self):
         super().setUp()
         self._num_of_sites = 2
         self._seed = 0
-        self._graph = nx.convert_matrix.from_numpy_matrix(np.array([[0, -1], [-1, 0]]))
-        self._new_disorder_graph = nx.convert_matrix.from_numpy_matrix(np.array([[0, 1], [1, 0]]))
+        self._graph = nx.convert_matrix.from_numpy_array(np.array([[0, -1], [-1, 0]]))
+        self._new_disorder_graph = nx.convert_matrix.from_numpy_array(np.array([[0, 1], [1, 0]]))
 
         op = QuadraticProgram()
         for _ in range(2):
diff --git a/test/converters/test_converters.py b/test/converters/test_converters.py
index 7290f3877..d65197712 100644
--- a/test/converters/test_converters.py
+++ b/test/converters/test_converters.py
@@ -1,6 +1,6 @@
 # This code is part of Qiskit.
 #
-# (C) Copyright IBM 2020, 2022.
+# (C) Copyright IBM 2020, 2023.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -17,15 +17,12 @@
 
 import numpy as np
 from docplex.mp.model import Model
-from qiskit.algorithms import NumPyMinimumEigensolver
-from qiskit.opflow import Z, I
+from qiskit.algorithms.minimum_eigensolvers import NumPyMinimumEigensolver
+from qiskit.opflow import I, Z
+
 import qiskit_optimization.optionals as _optionals
-from qiskit_optimization import QuadraticProgram, QiskitOptimizationError
-from qiskit_optimization.algorithms import (
-    MinimumEigenOptimizer,
-    CplexOptimizer,
-    ADMMOptimizer,
-)
+from qiskit_optimization import QiskitOptimizationError, QuadraticProgram
+from qiskit_optimization.algorithms import ADMMOptimizer, CplexOptimizer, MinimumEigenOptimizer
 from qiskit_optimization.algorithms.admm_optimizer import ADMMParameters
 from qiskit_optimization.converters import (
     InequalityToEquality,
@@ -36,7 +33,6 @@
 from qiskit_optimization.problems import Constraint, Variable
 from qiskit_optimization.translators import from_docplex_mp
 
-
 QUBIT_OP_MAXIMIZE_SAMPLE = (
     -199999.5 * (I ^ I ^ I ^ Z)
     + -399999.5 * (I ^ I ^ Z ^ I)
@@ -66,7 +62,7 @@ def test_empty_problem(self):
         op = conv.convert(op)
         conv = MaximizeToMinimize()
         op = conv.convert(op)
-        _, shift = op.to_ising()
+        _, shift = op.to_ising(opflow=True)
         self.assertEqual(shift, 0.0)
 
     def test_valid_variable_type(self):
@@ -75,12 +71,12 @@ def test_valid_variable_type(self):
         with self.assertRaises(QiskitOptimizationError):
             op = QuadraticProgram()
             op.integer_var(0, 10, "int_var")
-            _ = op.to_ising()
+            _ = op.to_ising(opflow=True)
         # Continuous variable
         with self.assertRaises(QiskitOptimizationError):
             op = QuadraticProgram()
             op.continuous_var(0, 10, "continuous_var")
-            _ = op.to_ising()
+            _ = op.to_ising(opflow=True)
 
     def test_inequality_binary(self):
         """Test InequalityToEqualityConverter with binary variables"""
@@ -401,7 +397,7 @@ def test_optimizationproblem_to_ising(self):
         op.linear_constraint(linear, Constraint.Sense.EQ, 3, "sum1")
         penalize = LinearEqualityToPenalty(penalty=1e5)
         op2 = penalize.convert(op)
-        qubitop, offset = op2.to_ising()
+        qubitop, offset = op2.to_ising(opflow=True)
         self.assertEqual(qubitop, QUBIT_OP_MAXIMIZE_SAMPLE)
         self.assertEqual(offset, OFFSET_MAXIMIZE_SAMPLE)
 
diff --git a/test/runtime/test_qaoaprogram.py b/test/runtime/test_qaoaclient.py
similarity index 62%
rename from test/runtime/test_qaoaprogram.py
rename to test/runtime/test_qaoaclient.py
index a3f172a63..d3880a6d2 100644
--- a/test/runtime/test_qaoaprogram.py
+++ b/test/runtime/test_qaoaclient.py
@@ -1,6 +1,6 @@
 # This code is part of Qiskit.
 #
-# (C) Copyright IBM 2021.
+# (C) Copyright IBM 2021, 2023.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -10,31 +10,26 @@
 # copyright notice, and modified files need to carry a notice indicating
 # that they have been altered from the originals.
 
-"""Test the QAOA program."""
+"""Test the QAOA client."""
 
+import unittest
+import warnings
 from test import QiskitOptimizationTestCase
 
-import warnings
-import unittest
-from ddt import ddt, data
 import numpy as np
+from ddt import data, ddt
 from qiskit.algorithms.optimizers import COBYLA
-from qiskit.providers.basicaer import QasmSimulatorPy
 from qiskit.opflow import I, Z
+from qiskit.providers.basicaer import QasmSimulatorPy
 
-from qiskit_optimization.runtime import (
-    QAOAClient,
-    QAOAProgram,
-    VQERuntimeResult,
-    VQEProgramResult,
-)
+from qiskit_optimization.runtime import QAOAClient, VQERuntimeResult
 
 from .fake_vqeruntime import FakeQAOARuntimeProvider
 
 
 @ddt
-class TestQAOAProgram(QiskitOptimizationTestCase):
-    """Test the QAOA program."""
+class TestQAOAClient(QiskitOptimizationTestCase):
+    """Test the QAOA client."""
 
     def setUp(self):
         super().setUp()
@@ -46,34 +41,23 @@ def setUp(self):
     )
     def test_standard_case(self, optimizer):
         """Test a standard use case."""
-        operator = Z ^ I ^ Z
         reps = 2
         initial_point = np.random.RandomState(42).random(2 * reps)
         backend = QasmSimulatorPy()
 
-        for use_deprecated in [False, True]:
-            if use_deprecated:
-                qaoa_cls = QAOAProgram
-                result_cls = VQEProgramResult
-                warnings.filterwarnings("ignore", category=DeprecationWarning)
-            else:
-                qaoa_cls = QAOAClient
-                result_cls = VQERuntimeResult
-
-            qaoa = qaoa_cls(
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore", category=DeprecationWarning)
+            operator = Z ^ I ^ Z
+            qaoa = QAOAClient(
                 optimizer=optimizer,
                 reps=reps,
                 initial_point=initial_point,
                 backend=backend,
                 provider=self.provider,
             )
-
-            if use_deprecated:
-                warnings.filterwarnings("always", category=DeprecationWarning)
-
             result = qaoa.compute_minimum_eigenvalue(operator)
 
-            self.assertIsInstance(result, result_cls)
+        self.assertIsInstance(result, VQERuntimeResult)
 
 
 if __name__ == "__main__":
diff --git a/test/runtime/test_vqeprogram.py b/test/runtime/test_vqeclient.py
similarity index 64%
rename from test/runtime/test_vqeprogram.py
rename to test/runtime/test_vqeclient.py
index 15962172d..869a04da9 100644
--- a/test/runtime/test_vqeprogram.py
+++ b/test/runtime/test_vqeclient.py
@@ -1,6 +1,6 @@
 # This code is part of Qiskit.
 #
-# (C) Copyright IBM 2021.
+# (C) Copyright IBM 2021, 2023.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -10,32 +10,27 @@
 # copyright notice, and modified files need to carry a notice indicating
 # that they have been altered from the originals.
 
-"""Test the VQE program."""
-
-from test import QiskitOptimizationTestCase
+"""Test the VQE client."""
 
 import unittest
 import warnings
-from ddt import ddt, data
+from test import QiskitOptimizationTestCase
+
 import numpy as np
+from ddt import data, ddt
 from qiskit.algorithms.optimizers import COBYLA
-from qiskit.providers.basicaer import QasmSimulatorPy
 from qiskit.circuit.library import RealAmplitudes
 from qiskit.opflow import I, Z
+from qiskit.providers.basicaer import QasmSimulatorPy
 
-from qiskit_optimization.runtime import (
-    VQEClient,
-    VQERuntimeResult,
-    VQEProgram,
-    VQEProgramResult,
-)
+from qiskit_optimization.runtime import VQEClient, VQERuntimeResult
 
 from .fake_vqeruntime import FakeVQERuntimeProvider
 
 
 @ddt
-class TestVQEProgram(QiskitOptimizationTestCase):
-    """Test the VQE program."""
+class TestVQEClient(QiskitOptimizationTestCase):
+    """Test the VQE client."""
 
     def setUp(self):
         super().setUp()
@@ -48,33 +43,22 @@ def setUp(self):
     def test_standard_case(self, optimizer):
         """Test a standard use case."""
         circuit = RealAmplitudes(3)
-        operator = Z ^ I ^ Z
         initial_point = np.random.RandomState(42).random(circuit.num_parameters)
         backend = QasmSimulatorPy()
 
-        for use_deprecated in [False, True]:
-            if use_deprecated:
-                vqe_cls = VQEProgram
-                result_cls = VQEProgramResult
-                warnings.filterwarnings("ignore", category=DeprecationWarning)
-            else:
-                vqe_cls = VQEClient
-                result_cls = VQERuntimeResult
-
-            vqe = vqe_cls(
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore", category=DeprecationWarning)
+            operator = Z ^ I ^ Z
+            vqe = VQEClient(
                 ansatz=circuit,
                 optimizer=optimizer,
                 initial_point=initial_point,
                 backend=backend,
                 provider=self.provider,
             )
-
-            if use_deprecated:
-                warnings.filterwarnings("always", category=DeprecationWarning)
-
             result = vqe.compute_minimum_eigenvalue(operator)
 
-            self.assertIsInstance(result, result_cls)
+        self.assertIsInstance(result, VQERuntimeResult)
 
 
 if __name__ == "__main__":
diff --git a/test/translators/test_docplex_mp.py b/test/translators/test_docplex_mp.py
index 65761a054..48148c660 100644
--- a/test/translators/test_docplex_mp.py
+++ b/test/translators/test_docplex_mp.py
@@ -1,6 +1,6 @@
 # This code is part of Qiskit.
 #
-# (C) Copyright IBM 2021, 2022.
+# (C) Copyright IBM 2021, 2023.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -508,9 +508,9 @@ def test_indicator_constraints(self):
             mod = Model()
             x = mod.binary_var("x")
             y = mod.integer_var(lb=-1, ub=2, name="y")
-            mod.add_indicator(binary_var=x, active_value=1, linear_ct=(y == 1))
-            mod.add_indicator(binary_var=x, active_value=1, linear_ct=(y <= 1))
-            mod.add_indicator(binary_var=x, active_value=1, linear_ct=(y >= 1))
+            mod.add_indicator(binary_var=x, active_value=1, linear_ct=y == 1)
+            mod.add_indicator(binary_var=x, active_value=1, linear_ct=y <= 1)
+            mod.add_indicator(binary_var=x, active_value=1, linear_ct=y >= 1)
             quad_prog = from_docplex_mp(mod)
             self.assertEqual(quad_prog.get_num_linear_constraints(), 4)
 
@@ -528,7 +528,7 @@ def test_indicator_constraints(self):
             x = mod.binary_var("x")
             y = mod.integer_var(lb=-1, ub=2, name="y")
             z = mod.continuous_var(lb=-1, ub=2, name="z")
-            mod.add_indicator(binary_var=x, linear_ct=(x + y + 2 * z <= -10))
+            mod.add_indicator(binary_var=x, linear_ct=x + y + 2 * z <= -10)
             quad_prog = from_docplex_mp(mod)
             self.assertEqual(quad_prog.get_num_linear_constraints(), 1)
 
@@ -543,7 +543,7 @@ def test_indicator_constraints(self):
             x = mod.binary_var("x")
             y = mod.integer_var(lb=-1, ub=2, name="y")
             z = mod.continuous_var(lb=-1, ub=2, name="z")
-            mod.add_indicator(binary_var=x, linear_ct=(x + y + 2 * z >= 10))
+            mod.add_indicator(binary_var=x, linear_ct=x + y + 2 * z >= 10)
             quad_prog = from_docplex_mp(mod)
             self.assertEqual(quad_prog.get_num_linear_constraints(), 1)
 
@@ -560,7 +560,7 @@ def test_indicator_constraints(self):
             x = mod.binary_var("x")
             y = mod.integer_var(lb=-1, ub=2, name="y")
             z = mod.continuous_var(lb=-1, ub=2, name="z")
-            mod.add_indicator(binary_var=x, linear_ct=(x + y + 2 * z == 0))
+            mod.add_indicator(binary_var=x, linear_ct=x + y + 2 * z == 0)
             quad_prog = from_docplex_mp(mod)
             self.assertEqual(quad_prog.get_num_linear_constraints(), 2)
 
diff --git a/test/translators/test_ising.py b/test/translators/test_ising.py
index a7e845337..f9652a101 100644
--- a/test/translators/test_ising.py
+++ b/test/translators/test_ising.py
@@ -1,6 +1,6 @@
 # This code is part of Qiskit.
 #
-# (C) Copyright IBM 2021, 2022.
+# (C) Copyright IBM 2021, 2023.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -15,17 +15,30 @@
 from test.optimization_test_case import QiskitOptimizationTestCase
 
 import numpy as np
+from ddt import data, ddt
+from qiskit.opflow import I, OperatorBase, PauliSumOp, Z
+from qiskit.quantum_info import Pauli, SparsePauliOp
+from qiskit.quantum_info.operators.base_operator import BaseOperator
 
-from qiskit.opflow import PauliSumOp, I, Z
 from qiskit_optimization.exceptions import QiskitOptimizationError
 from qiskit_optimization.problems import QuadraticProgram
 from qiskit_optimization.translators import from_ising, to_ising
 
 
+@ddt
 class TestIsingTranslator(QiskitOptimizationTestCase):
     """Test from_ising and to_ising"""
 
-    def test_to_ising(self):
+    @staticmethod
+    def op_from_list(lst, opflow):
+        """generate an operator from a list"""
+        if opflow:
+            return PauliSumOp.from_list(lst)
+        else:
+            return SparsePauliOp.from_list(lst)
+
+    @data(True, False)
+    def test_to_ising(self, opflow):
         """test to_ising"""
 
         with self.subTest("minimize"):
@@ -35,8 +48,9 @@ def test_to_ising(self):
             q_p.binary_var(name="x")
             q_p.binary_var(name="y")
             q_p.minimize(linear={"x": 1}, quadratic={("x", "y"): 1})
-            op, offset = to_ising(q_p)
-            op_ref = PauliSumOp.from_list([("ZI", -0.25), ("IZ", -0.75), ("ZZ", 0.25)])
+            op, offset = to_ising(q_p, opflow)
+            self.assertIsInstance(op, OperatorBase if opflow else BaseOperator)
+            op_ref = SparsePauliOp.from_list([("ZI", -0.25), ("IZ", -0.75), ("ZZ", 0.25)])
             np.testing.assert_allclose(op.to_matrix(), op_ref.to_matrix())
             self.assertAlmostEqual(offset, 0.75)
 
@@ -47,12 +61,14 @@ def test_to_ising(self):
             q_p.binary_var(name="x")
             q_p.binary_var(name="y")
             q_p.maximize(linear={"x": 1}, quadratic={("x", "y"): 1})
-            op, offset = to_ising(q_p)
-            op_ref = PauliSumOp.from_list([("ZI", 0.25), ("IZ", 0.75), ("ZZ", -0.25)])
+            op, offset = to_ising(q_p, opflow)
+            self.assertIsInstance(op, OperatorBase if opflow else BaseOperator)
+            op_ref = SparsePauliOp.from_list([("ZI", 0.25), ("IZ", 0.75), ("ZZ", -0.25)])
             np.testing.assert_allclose(op.to_matrix(), op_ref.to_matrix())
             self.assertAlmostEqual(offset, -0.75)
 
-    def test_to_ising2(self):
+    @data(True, False)
+    def test_to_ising2(self, opflow):
         """test to_ising 2"""
 
         with self.subTest("minimize"):
@@ -62,8 +78,9 @@ def test_to_ising2(self):
             q_p.binary_var(name="x")
             q_p.binary_var(name="y")
             q_p.minimize(constant=1, linear={"x": -2, "y": -2}, quadratic={("x", "y"): 4})
-            op, offset = to_ising(q_p)
-            op_ref = PauliSumOp.from_list([("ZZ", 1.0)])
+            op, offset = to_ising(q_p, opflow)
+            self.assertIsInstance(op, OperatorBase if opflow else BaseOperator)
+            op_ref = SparsePauliOp.from_list([("ZZ", 1.0)])
             np.testing.assert_allclose(op.to_matrix(), op_ref.to_matrix())
             self.assertAlmostEqual(offset, 0.0)
 
@@ -74,16 +91,18 @@ def test_to_ising2(self):
             q_p.binary_var(name="x")
             q_p.binary_var(name="y")
             q_p.maximize(constant=1, linear={"x": -2, "y": -2}, quadratic={("x", "y"): 4})
-            op, offset = to_ising(q_p)
-            op_ref = PauliSumOp.from_list([("ZZ", -1.0)])
+            op, offset = to_ising(q_p, opflow)
+            self.assertIsInstance(op, OperatorBase if opflow else BaseOperator)
+            op_ref = SparsePauliOp.from_list([("ZZ", -1.0)])
             np.testing.assert_allclose(op.to_matrix(), op_ref.to_matrix())
             self.assertAlmostEqual(offset, 0.0)
 
-    def test_from_ising(self):
+    @data(True, False)
+    def test_from_ising(self, opflow):
         """test from_ising"""
         # minimize: x + x * y
         # subject to: x, y \in {0, 1}
-        op = PauliSumOp.from_list([("ZI", -0.25), ("IZ", -0.75), ("ZZ", 0.25)])
+        op = self.op_from_list([("ZI", -0.25), ("IZ", -0.75), ("ZZ", 0.25)], opflow)
         with self.subTest("linear: True"):
             q_p = from_ising(op, 0.75, linear=True)
             self.assertEqual(q_p.get_num_vars(), op.num_qubits)
@@ -102,11 +121,12 @@ def test_from_ising(self):
             np.testing.assert_allclose(q_p.objective.linear.to_array(), [0, 0])
             np.testing.assert_allclose(q_p.objective.quadratic.to_array(), [[1, 1], [0, 0]])
 
-    def test_from_ising2(self):
+    @data(True, False)
+    def test_from_ising2(self, opflow):
         """test from_ising 2"""
         # minimize: 1 - 2 * x1 - 2 * x2 + 4 * x1 * x2
         # subject to: x, y \in {0, 1}
-        op = PauliSumOp.from_list([("ZZ", 1)])
+        op = self.op_from_list([("ZZ", 1)], opflow)
         with self.subTest("linear: True"):
             q_p = from_ising(op, 0, linear=True)
             self.assertEqual(q_p.get_num_vars(), op.num_qubits)
@@ -125,28 +145,33 @@ def test_from_ising2(self):
             np.testing.assert_allclose(q_p.objective.linear.to_array(), [0, 0])
             np.testing.assert_allclose(q_p.objective.quadratic.to_array(), [[-2, 4], [0, -2]])
 
-    def test_from_ising_pauli_with_invalid_paulis(self):
+    @data(True, False)
+    def test_from_ising_pauli_with_invalid_paulis(self, opflow):
         """test from_ising with invalid Pauli terms"""
         with self.assertRaises(QiskitOptimizationError):
-            op = PauliSumOp.from_list([("IX", 1)])
+            op = self.op_from_list([("IX", 1)], opflow)
             _ = from_ising(op, 0)
 
         with self.assertRaises(QiskitOptimizationError):
-            op = PauliSumOp.from_list([("IY", 1)])
+            op = self.op_from_list([("IY", 1)], opflow)
             _ = from_ising(op, 0)
 
         with self.assertRaises(QiskitOptimizationError):
-            op = PauliSumOp.from_list([("ZZZ", 1)])
+            op = self.op_from_list([("ZZZ", 1)], opflow)
             _ = from_ising(op, 0)
 
         with self.assertRaises(QiskitOptimizationError):
-            op = PauliSumOp.from_list([("IZ", 1j)])
+            op = self.op_from_list([("IZ", 1j)], opflow)
             _ = from_ising(op, 0)
 
-    def test_pauli_I_Z(self):
+    @data(True, False)
+    def test_pauli_I_Z(self, opflow):
         """test from_ising and to_ising with Pauli I and Z"""
         with self.subTest("0 * I, linear=False"):
-            q_p = from_ising(0 * I, linear=False)
+            if opflow:
+                q_p = from_ising(SparsePauliOp("I", 0), linear=False)
+            else:
+                q_p = from_ising(0 * I, linear=False)
             self.assertEqual(q_p.get_num_vars(), 1)
             self.assertEqual(q_p.get_num_linear_constraints(), 0)
             self.assertEqual(q_p.get_num_quadratic_constraints(), 0)
@@ -154,12 +179,15 @@ def test_pauli_I_Z(self):
             np.testing.assert_allclose(q_p.objective.linear.to_array(), [0])
             np.testing.assert_allclose(q_p.objective.quadratic.to_array(), [[0]])
 
-            op, offset = to_ising(q_p)
+            op, offset = to_ising(q_p, opflow=True)
             np.testing.assert_allclose(op.to_matrix(), np.zeros((2, 2)))
             self.assertAlmostEqual(offset, 0)
 
         with self.subTest("0 * I, linear=True"):
-            q_p = from_ising(0 * I, linear=True)
+            if opflow:
+                q_p = from_ising(SparsePauliOp("I", 0), linear=True)
+            else:
+                q_p = from_ising(0 * I, linear=True)
             self.assertEqual(q_p.get_num_vars(), 1)
             self.assertEqual(q_p.get_num_linear_constraints(), 0)
             self.assertEqual(q_p.get_num_quadratic_constraints(), 0)
@@ -167,12 +195,15 @@ def test_pauli_I_Z(self):
             np.testing.assert_allclose(q_p.objective.linear.to_array(), [0])
             np.testing.assert_allclose(q_p.objective.quadratic.to_array(), [[0]])
 
-            op, offset = to_ising(q_p)
+            op, offset = to_ising(q_p, opflow=True)
             np.testing.assert_allclose(op.to_matrix(), np.zeros((2, 2)))
             self.assertAlmostEqual(offset, 0)
 
         with self.subTest("2 * I, linear=False"):
-            q_p = from_ising(2 * I, linear=False)
+            if opflow:
+                q_p = from_ising(SparsePauliOp("I", 2), linear=False)
+            else:
+                q_p = from_ising(2 * I, linear=False)
             self.assertEqual(q_p.get_num_vars(), 1)
             self.assertEqual(q_p.get_num_linear_constraints(), 0)
             self.assertEqual(q_p.get_num_quadratic_constraints(), 0)
@@ -180,12 +211,15 @@ def test_pauli_I_Z(self):
             np.testing.assert_allclose(q_p.objective.linear.to_array(), [0])
             np.testing.assert_allclose(q_p.objective.quadratic.to_array(), [[0]])
 
-            op, offset = to_ising(q_p)
+            op, offset = to_ising(q_p, opflow=True)
             np.testing.assert_allclose(op.to_matrix(), np.zeros((2, 2)))
             self.assertAlmostEqual(offset, 2)
 
         with self.subTest("2 * I, linear=True"):
-            q_p = from_ising(2 * I, linear=True)
+            if opflow:
+                q_p = from_ising(SparsePauliOp("I", 2), linear=True)
+            else:
+                q_p = from_ising(2 * I, linear=True)
             self.assertEqual(q_p.get_num_vars(), 1)
             self.assertEqual(q_p.get_num_linear_constraints(), 0)
             self.assertEqual(q_p.get_num_quadratic_constraints(), 0)
@@ -193,12 +227,15 @@ def test_pauli_I_Z(self):
             np.testing.assert_allclose(q_p.objective.linear.to_array(), [0])
             np.testing.assert_allclose(q_p.objective.quadratic.to_array(), [[0]])
 
-            op, offset = to_ising(q_p)
+            op, offset = to_ising(q_p, opflow=True)
             np.testing.assert_allclose(op.to_matrix(), np.zeros((2, 2)))
             self.assertAlmostEqual(offset, 2)
 
         with self.subTest("Z, linear=False"):
-            q_p = from_ising(Z)
+            if opflow:
+                q_p = from_ising(Pauli("Z"), linear=False)
+            else:
+                q_p = from_ising(Z, linear=False)
             self.assertEqual(q_p.get_num_vars(), 1)
             self.assertEqual(q_p.get_num_linear_constraints(), 0)
             self.assertEqual(q_p.get_num_quadratic_constraints(), 0)
@@ -206,12 +243,15 @@ def test_pauli_I_Z(self):
             np.testing.assert_allclose(q_p.objective.linear.to_array(), [0])
             np.testing.assert_allclose(q_p.objective.quadratic.to_array(), [[-2]])
 
-            op, offset = to_ising(q_p)
+            op, offset = to_ising(q_p, opflow=True)
             np.testing.assert_allclose(op.to_matrix(), Z.to_matrix())
             self.assertAlmostEqual(offset, 0)
 
         with self.subTest("Z, linear=True"):
-            q_p = from_ising(Z, linear=True)
+            if opflow:
+                q_p = from_ising(Pauli("Z"), linear=True)
+            else:
+                q_p = from_ising(Z, linear=True)
             self.assertEqual(q_p.get_num_vars(), 1)
             self.assertEqual(q_p.get_num_linear_constraints(), 0)
             self.assertEqual(q_p.get_num_quadratic_constraints(), 0)
@@ -219,12 +259,15 @@ def test_pauli_I_Z(self):
             np.testing.assert_allclose(q_p.objective.linear.to_array(), [-2])
             np.testing.assert_allclose(q_p.objective.quadratic.to_array(), [[0]])
 
-            op, offset = to_ising(q_p)
+            op, offset = to_ising(q_p, opflow=True)
             np.testing.assert_allclose(op.to_matrix(), Z.to_matrix())
             self.assertAlmostEqual(offset, 0)
 
         with self.subTest("3 * II, linear=False"):
-            q_p = from_ising(3 * I ^ I)
+            if opflow:
+                q_p = from_ising(SparsePauliOp("II", 3), linear=False)
+            else:
+                q_p = from_ising(3 * I ^ I, linear=False)
             self.assertEqual(q_p.get_num_vars(), 2)
             self.assertEqual(q_p.get_num_linear_constraints(), 0)
             self.assertEqual(q_p.get_num_quadratic_constraints(), 0)
@@ -232,12 +275,15 @@ def test_pauli_I_Z(self):
             np.testing.assert_allclose(q_p.objective.linear.to_array(), [0, 0])
             np.testing.assert_allclose(q_p.objective.quadratic.to_array(), [[0, 0], [0, 0]])
 
-            op, offset = to_ising(q_p)
+            op, offset = to_ising(q_p, opflow=True)
             np.testing.assert_allclose(op.to_matrix(), np.zeros((4, 4)))
             self.assertAlmostEqual(offset, 3)
 
         with self.subTest("3 * II, linear=True"):
-            q_p = from_ising(3 * I ^ I)
+            if opflow:
+                q_p = from_ising(SparsePauliOp("II", 3), linear=True)
+            else:
+                q_p = from_ising(3 * I ^ I, linear=True)
             self.assertEqual(q_p.get_num_vars(), 2)
             self.assertEqual(q_p.get_num_linear_constraints(), 0)
             self.assertEqual(q_p.get_num_quadratic_constraints(), 0)
@@ -245,12 +291,15 @@ def test_pauli_I_Z(self):
             np.testing.assert_allclose(q_p.objective.linear.to_array(), [0, 0])
             np.testing.assert_allclose(q_p.objective.quadratic.to_array(), [[0, 0], [0, 0]])
 
-            op, offset = to_ising(q_p)
+            op, offset = to_ising(q_p, opflow=True)
             np.testing.assert_allclose(op.to_matrix(), np.zeros((4, 4)))
             self.assertAlmostEqual(offset, 3)
 
         with self.subTest("IZ, linear=False"):
-            q_p = from_ising(I ^ Z)
+            if opflow:
+                q_p = from_ising(Pauli("IZ"), linear=False)
+            else:
+                q_p = from_ising(I ^ Z, linear=False)
             self.assertEqual(q_p.get_num_vars(), 2)
             self.assertEqual(q_p.get_num_linear_constraints(), 0)
             self.assertEqual(q_p.get_num_quadratic_constraints(), 0)
@@ -258,12 +307,15 @@ def test_pauli_I_Z(self):
             np.testing.assert_allclose(q_p.objective.linear.to_array(), [0, 0])
             np.testing.assert_allclose(q_p.objective.quadratic.to_array(), [[-2, 0], [0, 0]])
 
-            op, offset = to_ising(q_p)
+            op, offset = to_ising(q_p, opflow=True)
             np.testing.assert_allclose(op.to_matrix(), (I ^ Z).to_matrix())
             self.assertAlmostEqual(offset, 0)
 
         with self.subTest("IZ, linear=True"):
-            q_p = from_ising(I ^ Z, linear=True)
+            if opflow:
+                q_p = from_ising(Pauli("IZ"), linear=True)
+            else:
+                q_p = from_ising(I ^ Z, linear=True)
             self.assertEqual(q_p.get_num_vars(), 2)
             self.assertEqual(q_p.get_num_linear_constraints(), 0)
             self.assertEqual(q_p.get_num_quadratic_constraints(), 0)
@@ -271,28 +323,39 @@ def test_pauli_I_Z(self):
             np.testing.assert_allclose(q_p.objective.linear.to_array(), [-2, 0])
             np.testing.assert_allclose(q_p.objective.quadratic.to_array(), [[0, 0], [0, 0]])
 
-            op, offset = to_ising(q_p)
+            op, offset = to_ising(q_p, opflow=True)
             np.testing.assert_allclose(op.to_matrix(), (I ^ Z).to_matrix())
             self.assertAlmostEqual(offset, 0)
 
-    def test_to_ising_wo_variable(self):
+    @data(True, False)
+    def test_to_ising_wo_variable(self, opflow):
         """test to_ising with problems without variables"""
         with self.subTest("empty problem"):
             q_p = QuadraticProgram()
-            op, offset = to_ising(q_p)
+            op, offset = to_ising(q_p, opflow)
             np.testing.assert_allclose(op.to_matrix(), np.zeros((2, 2)))
             self.assertAlmostEqual(offset, 0)
 
         with self.subTest("min 3"):
             q_p = QuadraticProgram()
             q_p.minimize(constant=3)
-            op, offset = to_ising(q_p)
+            op, offset = to_ising(q_p, opflow)
             np.testing.assert_allclose(op.to_matrix(), np.zeros((2, 2)))
             self.assertAlmostEqual(offset, 3)
 
         with self.subTest("max -1"):
             q_p = QuadraticProgram()
             q_p.maximize(constant=-1)
-            op, offset = to_ising(q_p)
+            op, offset = to_ising(q_p, opflow)
             np.testing.assert_allclose(op.to_matrix(), np.zeros((2, 2)))
             self.assertAlmostEqual(offset, 1)
+
+    def test_warning(self):
+        """Test warning message"""
+        q_p = QuadraticProgram()
+        with self.assertWarns(UserWarning):
+            _ = to_ising(q_p)
+
+        op = PauliSumOp.from_list([("Z", 1)])
+        with self.assertWarns(PendingDeprecationWarning):
+            _ = from_ising(op)
diff --git a/tools/check_copyright.py b/tools/check_copyright.py
index d3a50d311..003437e3f 100644
--- a/tools/check_copyright.py
+++ b/tools/check_copyright.py
@@ -1,6 +1,6 @@
 # This code is part of Qiskit.
 #
-# (C) Copyright IBM 2020, 2022.
+# (C) Copyright IBM 2020, 2023.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -13,6 +13,7 @@
 """ Fix copyright year in header """
 
 from typing import Tuple, Union, List
+import builtins
 import sys
 import os
 import datetime
@@ -75,7 +76,7 @@ def _cmd_execute(self, args: List[str]) -> Tuple[str, Union[None, str]]:
     def _get_changed_files(self) -> List[str]:
         out_str, err_str = self._cmd_execute(["git", "diff", "--name-only", "HEAD"])
         if err_str:
-            raise Exception(err_str)
+            raise builtins.Exception(err_str)
 
         return out_str.splitlines()
 
@@ -89,7 +90,7 @@ def _get_file_last_year(self, relative_path: str) -> int:
             last_year = CopyrightChecker._get_year_from_date(out_str)
             if err_str:
                 errors.append(err_str)
-        except Exception as ex:  # pylint: disable=broad-except
+        except builtins.Exception as ex:  # pylint: disable=broad-except
             errors.append(f"'{relative_path}' Last year: {str(ex)}")
 
         if errors:
diff --git a/tools/deploy_documentation.sh b/tools/deploy_documentation.sh
index 96b903064..60a5f236b 100755
--- a/tools/deploy_documentation.sh
+++ b/tools/deploy_documentation.sh
@@ -2,7 +2,7 @@
 
 # This code is part of Qiskit.
 #
-# (C) Copyright IBM 2018, 2022.
+# (C) Copyright IBM 2018, 2023.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -12,7 +12,7 @@
 # copyright notice, and modified files need to carry a notice indicating
 # that they have been altered from the originals.
 
-# Script for pushing the documentation to the qiskit.org repository.
+# Script for pushing the documentation to qiskit.org/ecosystem.
 set -e
 
 curl https://downloads.rclone.org/rclone-current-linux-amd64.deb -o rclone.deb
@@ -23,7 +23,10 @@ RCLONE_CONFIG_PATH=$(rclone config file | tail -1)
 echo "show current dir: "
 pwd
 
-# Push to qiskit.org website
+# Push to qiskit.org/ecosystem
 openssl aes-256-cbc -K $encrypted_rclone_key -iv $encrypted_rclone_iv -in tools/rclone.conf.enc -out $RCLONE_CONFIG_PATH -d
-echo "Pushing built docs to website"
-rclone sync --progress --exclude locale/** ./docs/_build/html IBMCOS:qiskit-org-web-resources/documentation/optimization
+echo "Pushing built docs to qiskit.org/ecosystem"
+rclone sync --progress --exclude locale/** ./docs/_build/html IBMCOS:qiskit-org-web-resources/ecosystem/optimization
+
+# Push to qiskit.org/documentation
+rclone sync --progress --exclude locale/** ./docs/_build/html IBMCOS:qiskit-org-web-resources/documentation/optimization
\ No newline at end of file
diff --git a/tools/ignore_untagged_notes.sh b/tools/ignore_untagged_notes.sh
index f76f6caea..b31071b0d 100755
--- a/tools/ignore_untagged_notes.sh
+++ b/tools/ignore_untagged_notes.sh
@@ -2,7 +2,7 @@
 
 # This code is part of Qiskit.
 #
-# (C) Copyright IBM 2021, 2022.
+# (C) Copyright IBM 2021, 2023.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -17,7 +17,7 @@
 LATEST_TAG=$(git describe --tags --abbrev=0)
 
 # select only release notes added after the tag was created
-for file_changed in `git diff --name-only --diff-filter=A HEAD $LATEST_TAG releasenotes/notes`
+for file_changed in `git diff --name-only HEAD $LATEST_TAG releasenotes/notes`
 do
     if [[ $file_changed == releasenotes/notes/* ]]; then
         isInFile=$(grep -Exq "\s*$file_changed," docs/release_notes.rst >/dev/null; echo $?)
diff --git a/tox.ini b/tox.ini
index 2826bea67..05e3b1fd0 100644
--- a/tox.ini
+++ b/tox.ini
@@ -1,6 +1,6 @@
 [tox]
-minversion = 2.1
-envlist = py37, py38, py39, py310, lint
+minversion = 3.3.0
+envlist = py37, py38, py39, py310, py311, lint
 skipsdist = True
 
 [testenv]
@@ -12,8 +12,12 @@ setenv =
   LC_ALL=en_US.utf-8
   ARGS="-V"
 deps = git+https://github.com/Qiskit/qiskit-terra.git
-       git+https://github.com/Qiskit/qiskit-aer.git
+       -r{toxinidir}/requirements.txt
        -r{toxinidir}/requirements-dev.txt
+       cplex
+       cvxpy
+       matplotlib
+       gurobipy
 commands =
   stestr run {posargs}
 
@@ -21,16 +25,16 @@ commands =
 envdir = .tox/lint
 basepython = python3
 commands =
-  black --check {posargs} qiskit_optimization test tools docs
+  black --check {posargs} qiskit_optimization test tools docs setup.py
   pylint -rn qiskit_optimization test tools
   mypy qiskit_optimization test tools
   python3 {toxinidir}/tools/check_copyright.py -path {toxinidir}
-  python3 {toxinidir}/tools/verify_headers.py qiskit_optimization test tools
+  python3 {toxinidir}/tools/verify_headers.py qiskit_optimization test tools setup.py
   python3 {toxinidir}/tools/find_stray_release_notes.py
 
 [testenv:black]
 envdir = .tox/lint
-commands = black {posargs} qiskit_optimization test tools docs
+commands = black {posargs} qiskit_optimization test tools docs setup.py
 
 [testenv:coverage]
 basepython = python3
@@ -51,8 +55,10 @@ commands =
 [testenv:gettext]
 envdir = .tox/docs
 deps =
-  -r requirements-dev.txt
+  -r{toxinidir}/requirements.txt
+  -r{toxinidir}/requirements-dev.txt
   sphinx-intl
+  jupyter
 commands =
   sphinx-build -W -T --keep-going -b gettext docs/ docs/_build/gettext {posargs}
   sphinx-intl -c docs/conf.py update -p docs/_build/gettext -l en -d docs/locale