add migration guide to v0.6

docs/migration/01_migration_guide_to_v0.5.rst

@@ -55,23 +55,14 @@ an extension that allows users to access the eigen states.
 
 The following is the corresponding table.
 
-+-------------------------------------+--------------------------------+
-| Former algorithm                    | New algorithm                  |
-+=====================================+================================+
-| ``qis                               | ``qiskit.                      |
-| kit.algorithms.MinimumEigenSolver`` | algorithms.minimum_eigensolver |
-|                                     | s.SamplingMinimumEigensolver`` |
-+-------------------------------------+--------------------------------+
-| ``qiskit.a                          | ``qis                          |
-| lgorithms.NumPyMinimumEigensolver`` | kit.algorithms.minimum_eigenso |
-|                                     | lver.NumPyMinimumEigensolver`` |
-+-------------------------------------+--------------------------------+
-| ``qiskit.algorithms.QAOA``          | ``qiskit.algorith              |
-|                                     | ms.minimum_eigensolvers.QAOA`` |
-+-------------------------------------+--------------------------------+
-| ``qiskit.algorithms.VQE``           | ``qiskit.algorithms.mini       |
-|                                     | mum_eigensolvers.SamplingVQE`` |
-+-------------------------------------+--------------------------------+
+.. csv-table::
+    :header: Former algorithm, New algorithm
+
+    ``qiskit.algorithms.MinimumEigenSolver``, ``qiskit.algorithms.minimum_eigensolvers.SamplingMinimumEigensolver``
+    ``qiskit.algorithms.NumPyMinimumEigensolver``, ``qiskit.algorithms.minimum_eigensolver.NumPyMinimumEigensolver``
+    ``qiskit.algorithms.QAOA``, ``qiskit.algorithms.minimum_eigensolvers.QAOA``
+    ``qiskit.algorithms.VQE``, ``qiskit.algorithms.minimum_eigensolvers.SamplingVQE``
+
 
 Setup of a problem
 

docs/migration/02_migration_guide_to_v0.6.rst

@@ -0,0 +1,235 @@
+Qiskit Optimization v0.6 Migration Guide
+========================================
+
+This tutorial will guide you through the process of migrating your code
+from Qiskit Optimization v0.5 to v0.6.
+
+Overview
+--------
+
+Qiskit Terra v0.25 deprecated the ``qiskit.algorithms`` module. It has been
+superseded by a new standalone library
+`Qiskit Algorithms <https://github.com/qiskit-community/qiskit_algorithms>`__.
+
+Qiskit Optimization v0.6 supports only the new algorithms of Qiskit Algorithms.
+
+It is not the intention to provide detailed explanations of the
+new Qiskit Algorithms in this migration guide. We suggest that you read the
+`corresponding
+resources <https://qiskit.org/ecosystem/algorithms/index.html>`__
+of the Qiskit Algorithms documentation instead.
+
+We can basically use the existing codes by replacing ``qiskit.algorithms``
+with ``qiskit_algorithms``.
+
+
+``MinimumEigenOptimizer``
+-------------------------
+
+The former algorithms exist in
+``qiskit.algorithms.minimum_eigensolvers``.
+On the other hand, the new algorithms exist in
+``qiskit_algorithms.minimum_eigensolvers`` and we can access them by
+``qiskit_algorithms.*``.
+
+``MinimumEigenOptimizer`` of Qiskit Optimization can use
+``qiskit_algorithms.SamplingMinimumEigensolver``
+interface of the new algorithms. Note that ``MinimumEigenOptimizer``
+cannot basically handle
+``qiskit_algorithms.MinimumEigensolver`` of the new
+algorithms. But there is an exception. ``MinimumEigenOptimizer`` can
+handle ``qiskit_algorithms.NumPyMinimumEigensolver``
+because ``qiskit_algorithms.NumPyMinimumEigensolver`` has
+an extension that allows users to access the eigen states.
+
+The following is the corresponding table.
+
+.. csv-table::
+    :header: Former algorithm, New algorithm
+
+    ``qiskit.algorithms.minimum_eigensolvers.SamplingMinimumEigensolver``, ``qiskit_algorithms.SamplingMinimumEigensolver``
+    ``qiskit.algorithms.minimum_eigensolver.NumPyMinimumEigensolver``, ``qiskit_algorithms.NumPyMinimumEigensolver``
+    ``qiskit.algorithms.minimum_eigensolvers.QAOA``, ``qiskit_algorithms.QAOA``
+    ``qiskit.algorithms.minimum_eigensolvers.SamplingVQE``, ``qiskit_algorithms.SamplingVQE``
+
+
+
+NumPyMinimumEigensolver
+~~~~~~~~~~~~~~~~~~~~~~~
+
+Previously
+
+.. code:: python
+
+    from qiskit.algorithms.minimum_eigensolvers import NumPyMinimumEigensolver
+
+    from qiskit_optimization.algorithms import MinimumEigenOptimizer
+
+    mes = NumPyMinimumEigensolver()
+    meo = MinimumEigenOptimizer(min_eigen_solver=mes)
+    result = meo.solve(problem)
+    print(result)
+
+
+New
+
+.. code:: python
+
+    from qiskit_algorithms import NumPyMinimumEigensolver
+
+    from qiskit_optimization.algorithms import MinimumEigenOptimizer
+
+    mes = NumPyMinimumEigensolver()
+    meo = MinimumEigenOptimizer(min_eigen_solver=mes)
+    result = meo.solve(problem)
+    print(result)
+
+
+
+QAOA
+~~~~
+
+Previously
+
+.. code:: python
+
+    from qiskit.algorithms.minimum_eigensolvers import QAOA
+    from qiskit.algorithms.optimizers import COBYLA
+    from qiskit.primitives import Sampler
+
+    from qiskit_optimization.algorithms import MinimumEigenOptimizer
+
+    shots = 1000
+    mes = QAOA(sampler=Sampler(), optimizer=COBYLA())
+    meo = MinimumEigenOptimizer(min_eigen_solver=mes)
+    result = meo.solve(problem)
+    print(result)
+
+
+New
+
+.. code:: python
+
+    from qiskit_algorithms import QAOA
+    from qiskit_algorithms.optimizers import COBYLA
+    from qiskit.primitives import Sampler
+
+    from qiskit_optimization.algorithms import MinimumEigenOptimizer
+
+    shots = 1000
+    mes = QAOA(sampler=Sampler(), optimizer=COBYLA())
+    meo = MinimumEigenOptimizer(min_eigen_solver=mes)
+    result = meo.solve(problem)
+    print(result)
+
+
+
+SamplingVQE
+~~~~~~~~~~~
+
+Previously
+
+.. code:: python
+
+    from qiskit.algorithms.minimum_eigensolvers import SamplingVQE
+    from qiskit.algorithms.optimizers import COBYLA
+    from qiskit.circuit.library import RealAmplitudes
+    from qiskit.primitives import Sampler
+
+    from qiskit_optimization.algorithms import MinimumEigenOptimizer
+
+    mes = SamplingVQE(sampler=Sampler(), ansatz=RealAmplitudes(), optimizer=COBYLA())
+    meo = MinimumEigenOptimizer(min_eigen_solver=mes)
+    result = meo.solve(problem)
+    print(result)
+
+
+New
+
+.. code:: python
+
+    from qiskit_algorithms import SamplingVQE
+    from qiskit_algorithms.optimizers import COBYLA
+    from qiskit.circuit.library import RealAmplitudes
+    from qiskit.primitives import Sampler
+
+    from qiskit_optimization.algorithms import MinimumEigenOptimizer
+
+    mes = SamplingVQE(sampler=Sampler(), ansatz=RealAmplitudes(), optimizer=COBYLA())
+    meo = MinimumEigenOptimizer(min_eigen_solver=mes)
+    result = meo.solve(problem)
+    print(result)
+
+
+
+``WarmStartQAOAOptimizer``
+--------------------------
+
+
+Previously
+
+.. code:: python
+
+    from qiskit.algorithms.minimum_eigensolvers import QAOA
+    from qiskit.algorithms.optimizers import COBYLA
+    from qiskit.primitives import Sampler
+
+    from qiskit_optimization.algorithms import WarmStartQAOAOptimizer, SlsqpOptimizer
+
+    qaoa = QAOA(sampler=Sampler(), optimizer=COBYLA())
+    optimizer = WarmStartQAOAOptimizer(
+        pre_solver=SlsqpOptimizer(), relax_for_pre_solver=True, qaoa=qaoa, epsilon=0.25
+    )
+    result = optimizer.solve(problem)
+    print(result)
+
+
+New
+
+.. code:: python
+
+    from qiskit_algorithms import QAOA
+    from qiskit_algorithms.optimizers import COBYLA
+    from qiskit.primitives import Sampler
+
+    from qiskit_optimization.algorithms import WarmStartQAOAOptimizer, SlsqpOptimizer
+
+    qaoa = QAOA(sampler=Sampler(), optimizer=COBYLA())
+    optimizer = WarmStartQAOAOptimizer(
+        pre_solver=SlsqpOptimizer(), relax_for_pre_solver=True, qaoa=qaoa, epsilon=0.25
+    )
+    result = optimizer.solve(problem)
+    print(result)
+
+
+
+``GroverOptimizer``
+-------------------
+
+
+Previously
+
+.. code:: python
+
+    from qiskit.algorithms.optimizers import COBYLA
+    from qiskit.primitives import Sampler
+
+    from qiskit_optimization.algorithms import GroverOptimizer
+
+    optimizer = GroverOptimizer(num_value_qubits=3, num_iterations=3, sampler=Sampler())
+    result = optimizer.solve(problem)
+    print(result)
+
+
+New
+
+.. code:: python
+
+    from qiskit_algorithms.optimizers import COBYLA
+    from qiskit.primitives import Sampler
+
+    from qiskit_optimization.algorithms import GroverOptimizer
+
+    optimizer = GroverOptimizer(num_value_qubits=3, num_iterations=3, sampler=Sampler())
+    result = optimizer.solve(problem)
+    print(result)