From 2be283e4e2657d995878704fdc7181e44b4f4c3c Mon Sep 17 00:00:00 2001 From: Stefano Carrazza Date: Sat, 3 Jul 2021 23:11:12 +0200 Subject: [PATCH 1/7] fixing syntax error --- doc/source/qibo.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/source/qibo.rst b/doc/source/qibo.rst index 41b3749452..43d151be10 100644 --- a/doc/source/qibo.rst +++ b/doc/source/qibo.rst @@ -275,7 +275,7 @@ First general unitary (U1) :member-order: bysource Second general unitary (U2) -^^^^^^^^^^^^^^^ +^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. autoclass:: qibo.abstractions.gates.U2 :members: From 7d849032a497d38d3c42242061efb02a90f1851c Mon Sep 17 00:00:00 2001 From: Stefano Carrazza Date: Sat, 3 Jul 2021 23:11:26 +0200 Subject: [PATCH 2/7] splitting docs by topic and application --- doc/source/applications.rst | 132 +++++++++++++++++++++++++++++++++--- 1 file changed, 124 insertions(+), 8 deletions(-) diff --git a/doc/source/applications.rst b/doc/source/applications.rst index 10c4d27aa2..7b9ae7f7a9 100644 --- a/doc/source/applications.rst +++ b/doc/source/applications.rst @@ -1,27 +1,143 @@ -Application tutorials -===================== +Applications +============ In this section we present some examples of quantum circuits applied to specific problems. +_______________________ + +Applications by topic +--------------------- + +Complexity theory +^^^^^^^^^^^^^^^^^ .. toctree:: :maxdepth: 1 - tutorials/aavqe/README.md tutorials/grover3sat/README.md + tutorials/adiabatic3sat/README.md + + +Condensed Matter Physics +^^^^^^^^^^^^^^^^^^^^^^^^ + +.. toctree:: + :maxdepth: 1 + + tutorials/aavqe/README.md + + +Cryptography +^^^^^^^^^^^^ + +.. toctree:: + :maxdepth: 1 + tutorials/hash-grover/README.md + + +Finance +^^^^^^^ + +.. toctree:: + :maxdepth: 1 + + tutorials/unary/README.md + + +High-Energy Physics +^^^^^^^^^^^^^^^^^^^ + +.. toctree:: + :maxdepth: 1 + + tutorials/qPDF/qPDF.ipynb + + +Quantum Physics +^^^^^^^^^^^^^^^ + +.. toctree:: + :maxdepth: 1 + + tutorials/qsvd/README.md + tutorials/3_tangle/README.md + tutorials/adiabatic/README.md + tutorials/shor/README.md + tutorials/bell-variational/README.md + tutorials/falqon/README.md + tutorials/grover/README.md + +Quantum Machine Learning +^^^^^^^^^^^^^^^^^^^^^^^^ + +.. toctree:: + :maxdepth: 1 + + tutorials/variational_classifier/README.md + tutorials/reuploading_classifier/README.md + tutorials/autoencoder/README.md + tutorials/EF_QAE/README.md + +_______________________ + +Applications by algorithm +------------------------- + +Variational Quantum Circuits +^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +.. toctree:: + :maxdepth: 1 + + tutorials/aavqe/README.md tutorials/variational_classifier/README.md tutorials/reuploading_classifier/README.md tutorials/autoencoder/README.md tutorials/qsvd/README.md tutorials/3_tangle/README.md + tutorials/EF_QAE/README.md tutorials/unary/README.md + tutorials/bell-variational/README.md + tutorials/qPDF/qPDF.ipynb + + +Grover's Algorithm +^^^^^^^^^^^^^^^^^^ + +.. toctree:: + :maxdepth: 1 + + tutorials/grover3sat/README.md + tutorials/hash-grover/README.md + tutorials/grover/README.md + + +Shor's Factorization Algorithms +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +.. toctree:: + :maxdepth: 1 + + tutorials/shor/README.md + + +Adiabatic Evolution +^^^^^^^^^^^^^^^^^^^ + +.. toctree:: + :maxdepth: 1 + tutorials/adiabatic/README.md tutorials/adiabatic3sat/README.md - tutorials/EF_QAE/README.md - tutorials/shor/README.md - tutorials/qPDF/qPDF.ipynb - tutorials/bell-variational/README.md + + +FALQON +^^^^^^ + +.. toctree:: + :maxdepth: 1 + tutorials/falqon/README.md - tutorials/grover/README.md \ No newline at end of file + From 32bdc9eaefb4504c2811aec7743be3229287218a Mon Sep 17 00:00:00 2001 From: Stefano Carrazza Date: Sat, 3 Jul 2021 23:46:54 +0200 Subject: [PATCH 3/7] adding qibojit --- setup.py | 1 + 1 file changed, 1 insertion(+) diff --git a/setup.py b/setup.py index 5b21dedb08..27fcc098d0 100644 --- a/setup.py +++ b/setup.py @@ -62,6 +62,7 @@ def get_version(): "tests": ["pytest", "cirq", "ply", "sklearn"], # Backends dependencies "qibotf": ["qibotf"], + "qibojit": ["qibojit"], "tensorflow": [f"tensorflow>={TF_MIN_VERSION}"], }, python_requires=">=3.6.0", From 285b4077e033669918007974598b6ea350424d5e Mon Sep 17 00:00:00 2001 From: Stefano Carrazza Date: Sat, 3 Jul 2021 23:47:05 +0200 Subject: [PATCH 4/7] updating installation instructions --- doc/source/backends.png | Bin 74203 -> 160990 bytes doc/source/installation.rst | 162 ++++++++++++++++++++++++++++++++---- 2 files changed, 148 insertions(+), 14 deletions(-) diff --git a/doc/source/backends.png b/doc/source/backends.png index bf4f18877eeca70894906162522ff7f3b583e5f2..6e3d22d5adbbb770c448956fc47c56e5475b32a9 100644 GIT binary patch literal 160990 zcmeFYbyOT((=R$ekOT{X;1V2yyIZi}!Civ8yAy&1A3SJ+1$XxV0fIZ3!QBTRoHOM4 zo;Nw?JL~>=*Iny=wHEaBp6=SUYnS}0x+mhDqBQz5!e;;g0A1#-gbM6n4*)#rL_va$ zfRaU=0069dZ#6AfmG>U69GxA^t!&L+xq3O8y)yH(G6w)WL1k%H&TIVDQV%9C+n&fr 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a/doc/source/installation.rst b/doc/source/installation.rst index a0aad6c9cf..85366dee2a 100644 --- a/doc/source/installation.rst +++ b/doc/source/installation.rst @@ -44,8 +44,8 @@ present a schematic view of the currently supported backends. .. image:: backends.png Quantum simulation is proposed through dedicated backends for single node -multi-GPU and multi-threading CPU setups. Quantum hardware control is under -development. +multi-GPU and multi-threading CPU setups. Quantum hardware control is supported +for chips based on superconducting qubits. _______________________ @@ -65,11 +65,22 @@ Base package Simulation backends ^^^^^^^^^^^^^^^^^^^ -We provide multiple simulation backends for Qibo, which are automatically loaded if the corresponding packages are installed, following the hierarchy below: +We provide multiple simulation backends for Qibo, which are automatically loaded +if the corresponding packages are installed, following the hierarchy below: * :ref:`installing-qibotf` (*recommended*): an efficient simulation backend for CPU, GPU and multi-GPU based on TensorFlow custom operators. Install this package if you need to simulate quantum circuits with large number of qubits or complex quantum algorithms which may benefit from computing parallelism. +* :ref:`installing-qibojit` (*experimental*): an efficient simulation backend for CPU, GPU and multi-GPU based on just-in-time (JIT) compiled custom operators. Install this package if you need to simulate quantum circuits with large number of qubits or complex quantum algorithms which may benefit from computing parallelism. * :ref:`installing-tensorflow`: a pure TensorFlow implementation for quantum simulation which provides access to gradient descent optimization and the possibility to implement classical and quantum architectures together. This backend is not optimized for memory and speed, use :ref:`installing-qibotf` instead. -* :ref:`installing-numpy`: a lightweight quantum simulator shipped with the :ref:`installing-qibo` base package. Use this simulator if your CPU architecture is not supported by the other backends. +* :ref:`installing-numpy`: a lightweight quantum simulator shipped with the :ref:`installing-qibo` base package. Use this simulator if your CPU architecture is not supported by the other backends. Please note that the simulation performance is quite poor in comparison to other backends. + +.. _hardware-backends: + +Hardware backends +^^^^^^^^^^^^^^^^^ + +We provide the following hardware control backends for Qibo: + +* :ref:`installing-qiboicarusq`: a module for laboratories, containing the specifics to operate Qibo on chips based on superconducting qubits, designed specifically for the IcarusQ experiment at `CQT `_. _______________________ @@ -79,15 +90,15 @@ Operating systems support In the table below we summarize the status of *pre-compiled binaries distributed with pypi* for the packages listed above. -+------------------+------+------------------+------------+ -| Operating System | qibo | qibotf (cpu/gpu) | tensorflow | -+==================+======+==================+============+ -| Linux x86 | Yes | Yes/Yes | Yes | -+------------------+------+------------------+------------+ -| MacOS >= 10.15 | Yes | Yes/No | Yes | -+------------------+------+------------------+------------+ -| Windows | Yes | No/No | Yes | -+------------------+------+------------------+------------+ ++------------------+------+------------------+---------+------------+ +| Operating System | qibo | qibotf (cpu/gpu) | qibojit | tensorflow | ++==================+======+==================+=========+============+ +| Linux x86 | Yes | Yes/Yes | Yes | Yes | ++------------------+------+------------------+---------+------------+ +| MacOS >= 10.15 | Yes | Yes/No | Yes | Yes | ++------------------+------+------------------+---------+------------+ +| Windows | Yes | No/No | Yes | Yes | ++------------------+------+------------------+---------+------------+ .. note:: All packages are supported for Python >= 3.6. @@ -175,7 +186,6 @@ dependencies. GPU, make sure TensorFlow runs on GPU, please refer to the `official documentation `_. -.. _installing-from-source: Installing from source """""""""""""""""""""" @@ -246,6 +256,79 @@ or if you prefer to manually execute all installation steps: _______________________ +.. _installing-qibojit: + +qibojit +^^^^^^^ + +The ``qibojit`` package contains a simulator implementation based on +just-in-time (JIT) custom kernels using `numba `_ +and `cupy `_. + +This backend is *experimental*, however, if needed, in order to switch to the +``qibojit`` backend please do: + +.. code-block:: python + + import qibo + qibo.set_backend("qibojit") + +Installing with pip +""""""""""""""""""" + +The installation using ``pip`` is the recommended approach to install +``qibojit``. + +In order to install the package use the following command: + +.. code-block:: bash + + pip install qibo[qibojit] + + +The ``pip`` program will download and install all the required +dependencies. + +.. note:: + The ``pip`` package requires a cupy pre-compiled version with CUDA + support, so make sure the `cupy `_ version installed + matches the specifics of your GPU and drivers. + + +Installing from source +"""""""""""""""""""""" + +The installation procedure presented in this section is useful if you have to +develop the code from source. + +In order to install the package perform the following steps: + +.. code-block:: + + git clone https://github.com/qiboteam/qibojit.git + cd qibojit + +then proceed with the installation of requirements with: + +.. code-block:: + + pip install -r requirements.txt + +Then proceed with the ``qibojit`` installation using ``pip`` + +.. code-block:: + + pip install . + +or if you prefer to manually execute all installation steps: + +.. code-block:: + + # builds binaries + python setup.py develop + +_______________________ + .. _installing-tensorflow: tensorflow @@ -291,3 +374,54 @@ please do: import qibo qibo.set_backend("numpy") + +_______________________ + +.. _installing-qiboicarusq: + +qiboicarusq +^^^^^^^^^^^ + +The ``qiboicarusq`` package contains the hardware control drivers for chips +based on superconducting qubits. More details are available at the code +`repository `_. + +This backend is designed for laboratories, in order to switch to the ``qiboicarusq`` +backend please do: + +.. code-block:: python + + import qibo + qibo.set_backend("qiboicarusq") + +Installing from source +"""""""""""""""""""""" + +The installation procedure presented in this section is useful if you have to +install and develop the code from source. + +In order to install the package perform the following steps: + +.. code-block:: + + git clone https://github.com/qiboteam/qiboicarusq.git + cd qiboicarusq + +then proceed with the installation of requirements with: + +.. code-block:: + + pip install -r requirements.txt + +Then proceed with the ``qiboicarusq`` installation using ``pip`` + +.. code-block:: + + pip install . + +or if you prefer to manually execute all installation steps: + +.. code-block:: + + # builds binaries + python setup.py develop From a90876129f54d5c0fb59cb71a6d2f3afce51bb01 Mon Sep 17 00:00:00 2001 From: Stefano Carrazza Date: Sun, 4 Jul 2021 12:54:04 +0200 Subject: [PATCH 5/7] changing backend order to match pull request 436 --- doc/source/installation.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/source/installation.rst b/doc/source/installation.rst index 85366dee2a..1b1162deba 100644 --- a/doc/source/installation.rst +++ b/doc/source/installation.rst @@ -68,8 +68,8 @@ Simulation backends We provide multiple simulation backends for Qibo, which are automatically loaded if the corresponding packages are installed, following the hierarchy below: -* :ref:`installing-qibotf` (*recommended*): an efficient simulation backend for CPU, GPU and multi-GPU based on TensorFlow custom operators. Install this package if you need to simulate quantum circuits with large number of qubits or complex quantum algorithms which may benefit from computing parallelism. * :ref:`installing-qibojit` (*experimental*): an efficient simulation backend for CPU, GPU and multi-GPU based on just-in-time (JIT) compiled custom operators. Install this package if you need to simulate quantum circuits with large number of qubits or complex quantum algorithms which may benefit from computing parallelism. +* :ref:`installing-qibotf`: an efficient simulation backend for CPU, GPU and multi-GPU based on TensorFlow custom operators. Install this package if you need to simulate quantum circuits with large number of qubits or complex quantum algorithms which may benefit from computing parallelism. * :ref:`installing-tensorflow`: a pure TensorFlow implementation for quantum simulation which provides access to gradient descent optimization and the possibility to implement classical and quantum architectures together. This backend is not optimized for memory and speed, use :ref:`installing-qibotf` instead. * :ref:`installing-numpy`: a lightweight quantum simulator shipped with the :ref:`installing-qibo` base package. Use this simulator if your CPU architecture is not supported by the other backends. Please note that the simulation performance is quite poor in comparison to other backends. 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z3#|W&{DJKLE$X!z|4LSY68<-RBJr!b>?i#HF8&M8|3&*ACjQHCzgXh`FHvilq1OXD iyR_RQ;QvnMi$Pv*Op3Jp8R~XV2D`4Ml7G!I=zjr9HGdHR diff --git a/doc/source/installation.rst b/doc/source/installation.rst index 1b1162deba..ef4b1a29aa 100644 --- a/doc/source/installation.rst +++ b/doc/source/installation.rst @@ -68,7 +68,7 @@ Simulation backends We provide multiple simulation backends for Qibo, which are automatically loaded if the corresponding packages are installed, following the hierarchy below: -* :ref:`installing-qibojit` (*experimental*): an efficient simulation backend for CPU, GPU and multi-GPU based on just-in-time (JIT) compiled custom operators. Install this package if you need to simulate quantum circuits with large number of qubits or complex quantum algorithms which may benefit from computing parallelism. +* :ref:`installing-qibojit`: an efficient simulation backend for CPU and GPU based on just-in-time (JIT) compiled custom operators. Install this package if you need to simulate quantum circuits with large number of qubits or complex quantum algorithms which may benefit from computing parallelism. * :ref:`installing-qibotf`: an efficient simulation backend for CPU, GPU and multi-GPU based on TensorFlow custom operators. Install this package if you need to simulate quantum circuits with large number of qubits or complex quantum algorithms which may benefit from computing parallelism. * :ref:`installing-tensorflow`: a pure TensorFlow implementation for quantum simulation which provides access to gradient descent optimization and the possibility to implement classical and quantum architectures together. This backend is not optimized for memory and speed, use :ref:`installing-qibotf` instead. * :ref:`installing-numpy`: a lightweight quantum simulator shipped with the :ref:`installing-qibo` base package. Use this simulator if your CPU architecture is not supported by the other backends. Please note that the simulation performance is quite poor in comparison to other backends. @@ -90,15 +90,15 @@ Operating systems support In the table below we summarize the status of *pre-compiled binaries distributed with pypi* for the packages listed above. -+------------------+------+------------------+---------+------------+ -| Operating System | qibo | qibotf (cpu/gpu) | qibojit | tensorflow | -+==================+======+==================+=========+============+ -| Linux x86 | Yes | Yes/Yes | Yes | Yes | -+------------------+------+------------------+---------+------------+ -| MacOS >= 10.15 | Yes | Yes/No | Yes | Yes | -+------------------+------+------------------+---------+------------+ -| Windows | Yes | No/No | Yes | Yes | -+------------------+------+------------------+---------+------------+ ++------------------+------+---------+------------------+------------+ +| Operating System | qibo | qibojit | qibotf (cpu/gpu) | tensorflow | ++==================+======+=========+==================+============+ +| Linux x86 | Yes | Yes | Yes/Yes | Yes | ++------------------+------+---------+------------------+------------+ +| MacOS >= 10.15 | Yes | Yes | Yes/No | Yes | ++------------------+------+---------+------------------+------------+ +| Windows | Yes | Yes | No/No | Yes | ++------------------+------+---------+------------------+------------+ .. note:: All packages are supported for Python >= 3.6. @@ -147,59 +147,57 @@ In order to install Qibo from source, you can simply clone the GitHub repository _______________________ -.. _installing-qibotf: +.. _installing-qibojit: -qibotf -^^^^^^ +qibojit +^^^^^^^ -The ``qibotf`` package contains a custom simulator implementation based on -TensorFlow and custom operators in CUDA/C++. +The ``qibojit`` package contains a simulator implementation based on +just-in-time (JIT) custom kernels using `numba `_ +and `cupy `_. This backend is used by default, however, if needed, in order to switch to the -``qibotf`` backend please do: +``qibojit`` backend please do: .. code-block:: python import qibo - qibo.set_backend("qibotf") + qibo.set_backend("qibojit") Installing with pip """"""""""""""""""" The installation using ``pip`` is the recommended approach to install -``qibotf``. We provide precompiled packages for linux x86/64 and macosx 10.15 or -greater for Python 3.6, 3.7, 3.8 and 3.9. +``qibojit``. In order to install the package use the following command: .. code-block:: bash - pip install qibo[qibotf] + pip install qibo[qibojit] + The ``pip`` program will download and install all the required dependencies. .. note:: - The ``pip`` packages for linux are compiled with CUDA support, so if your - system has a NVIDIA GPU, Qibo will perform calculations on GPU. Note that - ``qibotf`` uses TensorFlow for GPU management, if your system has a NVIDIA - GPU, make sure TensorFlow runs on GPU, please refer to the `official - documentation `_. + The ``pip`` package requires a cupy pre-compiled version with CUDA + support, so make sure the `cupy `_ version installed + matches the specifics of your GPU and drivers. Installing from source """""""""""""""""""""" -The installation procedure presented in this section is useful if the -pre-compiled binary packages for your operating system is not available or if -you have to develop the code from source. +The installation procedure presented in this section is useful if you have to +develop the code from source. In order to install the package perform the following steps: .. code-block:: - git clone https://github.com/qiboteam/qibotf.git - cd qibotf + git clone https://github.com/qiboteam/qibojit.git + cd qibojit then proceed with the installation of requirements with: @@ -207,38 +205,7 @@ then proceed with the installation of requirements with: pip install -r requirements.txt -Make sure your system has a GNU ``g++ >= 4`` compiler. If you are working on -macosx make sure the command ``c++`` is ``clang >= 11`` and install the libomp -library with ``brew install libomp`` command. - -Optionally, you can use the ``CXX`` environment variable to set then compiler -path. Similarly, the ``PYTHON`` environment variable sets the python interpreter -path. - -.. note:: - If your system has a NVIDIA GPU, make sure TensorFlow is installed - properly and runs on GPU, please refer to the `official - documentation `_. - - In that case, you can activate GPU support for Qibo by: - - 1. installing the NVCC compiler matching the TensorFlow CUDA version, see the `CUDA documentation `_. - - 2. exporting the ``CUDA_PATH`` variable with the CUDA installation path containing the cuda compiler. - - 3. make sure the NVCC compiler is available from ``CUDA_PATH/bin/nvcc``, otherwise the compilation may fail. You can locate it with ``whereis nvcc`` and eventually link/copy to your ``CUDA_PATH/bin`` folder. - - For example, TensorFlow 2.5.0 supports CUDA 11.2. After installing - TensorFlow proceed with the NVCC 11.2 installation. On linux the - installation path usually is ``/usr/local/cuda-11.2/``. - - Before installing Qibo do ``export CUDA_PATH=/usr/local/cuda-11.2``. - - Note that Qibo will not enable GPU support if points 1 and 2 are not - performed. - - -Then proceed with the ``qibotf`` installation using ``pip`` +Then proceed with the ``qibojit`` installation using ``pip`` .. code-block:: @@ -249,64 +216,62 @@ or if you prefer to manually execute all installation steps: .. code-block:: # builds binaries - python setup.py build - - # installs the Qibo packages - python setup.py install # or python setup.py develop + python setup.py deve _______________________ -.. _installing-qibojit: +.. _installing-qibotf: -qibojit -^^^^^^^ +qibotf +^^^^^^ -The ``qibojit`` package contains a simulator implementation based on -just-in-time (JIT) custom kernels using `numba `_ -and `cupy `_. +The ``qibotf`` package contains a custom simulator implementation based on +TensorFlow and custom operators in CUDA/C++. -This backend is *experimental*, however, if needed, in order to switch to the -``qibojit`` backend please do: +If needed, in order to switch to the ``qibotf`` backend please do: .. code-block:: python import qibo - qibo.set_backend("qibojit") + qibo.set_backend("qibotf") Installing with pip """"""""""""""""""" The installation using ``pip`` is the recommended approach to install -``qibojit``. +``qibotf``. We provide precompiled packages for linux x86/64 and macosx 10.15 or +greater for Python 3.6, 3.7, 3.8 and 3.9. In order to install the package use the following command: .. code-block:: bash - pip install qibo[qibojit] - + pip install qibo[qibotf] The ``pip`` program will download and install all the required dependencies. .. note:: - The ``pip`` package requires a cupy pre-compiled version with CUDA - support, so make sure the `cupy `_ version installed - matches the specifics of your GPU and drivers. + The ``pip`` packages for linux are compiled with CUDA support, so if your + system has a NVIDIA GPU, Qibo will perform calculations on GPU. Note that + ``qibotf`` uses TensorFlow for GPU management, if your system has a NVIDIA + GPU, make sure TensorFlow runs on GPU, please refer to the `official + documentation `_. Installing from source """""""""""""""""""""" -The installation procedure presented in this section is useful if you have to -develop the code from source. +The installation procedure presented in this section is useful if the +pre-compiled binary packages for your operating system is not available or if +you have to develop the code from source. In order to install the package perform the following steps: .. code-block:: - git clone https://github.com/qiboteam/qibojit.git - cd qibojit + git clone https://github.com/qiboteam/qibotf.git + cd qibotf then proceed with the installation of requirements with: @@ -314,7 +279,38 @@ then proceed with the installation of requirements with: pip install -r requirements.txt -Then proceed with the ``qibojit`` installation using ``pip`` +Make sure your system has a GNU ``g++ >= 4`` compiler. If you are working on +macosx make sure the command ``c++`` is ``clang >= 11`` and install the libomp +library with ``brew install libomp`` command. + +Optionally, you can use the ``CXX`` environment variable to set then compiler +path. Similarly, the ``PYTHON`` environment variable sets the python interpreter +path. + +.. note:: + If your system has a NVIDIA GPU, make sure TensorFlow is installed + properly and runs on GPU, please refer to the `official + documentation `_. + + In that case, you can activate GPU support for Qibo by: + + 1. installing the NVCC compiler matching the TensorFlow CUDA version, see the `CUDA documentation `_. + + 2. exporting the ``CUDA_PATH`` variable with the CUDA installation path containing the cuda compiler. + + 3. make sure the NVCC compiler is available from ``CUDA_PATH/bin/nvcc``, otherwise the compilation may fail. You can locate it with ``whereis nvcc`` and eventually link/copy to your ``CUDA_PATH/bin`` folder. + + For example, TensorFlow 2.5.0 supports CUDA 11.2. After installing + TensorFlow proceed with the NVCC 11.2 installation. On linux the + installation path usually is ``/usr/local/cuda-11.2/``. + + Before installing Qibo do ``export CUDA_PATH=/usr/local/cuda-11.2``. + + Note that Qibo will not enable GPU support if points 1 and 2 are not + performed. + + +Then proceed with the ``qibotf`` installation using ``pip`` .. code-block:: @@ -325,7 +321,12 @@ or if you prefer to manually execute all installation steps: .. code-block:: # builds binaries - python setup.py develop + python setup.py build + + # installs the Qibo packages + python setup.py install # or python setup.py develop + + _______________________ From 6ff50dcdfcd9421698627e8572340a062b25ee73 Mon Sep 17 00:00:00 2001 From: Stefano Carrazza Date: Sun, 4 Jul 2021 16:39:52 +0200 Subject: [PATCH 7/7] improving indexing --- doc/source/qibo.rst | 26 ++++++++++++++++++------ examples/benchmarks/results/ADIABATIC.md | 2 +- examples/benchmarks/results/QFT.md | 6 +++--- examples/benchmarks/results/SHOTS.md | 2 +- examples/benchmarks/results/VAR5.md | 16 +++++++-------- 5 files changed, 33 insertions(+), 19 deletions(-) diff --git a/doc/source/qibo.rst b/doc/source/qibo.rst index 43d151be10..7f4c41614e 100644 --- a/doc/source/qibo.rst +++ b/doc/source/qibo.rst @@ -145,22 +145,36 @@ Distributed circuit :members: :member-order: bysource -VQE -""" +Quantum Fourier Transform (QFT) +""""""""""""""""""""""""""""""" + +.. autoclass:: qibo.models.circuit.QFT + :members: + :member-order: bysource + +Variational Quantum Eigensolver (VQE) +""""""""""""""""""""""""""""""""""""" .. autoclass:: qibo.models.variational.VQE :members: :member-order: bysource -QAOA -"""" +Quantum Approximate Optimization Algorithm (QAOA) +""""""""""""""""""""""""""""""""""""""""""""""""" .. autoclass:: qibo.models.variational.QAOA :members: :member-order: bysource -Grover -"""""" +Feedback-based Algorithm for Quantum Optimization (FALQON) +"""""""""""""""""""""""""""""""""""""""""""""""""""""""""" + +.. autoclass:: qibo.models.variational.FALQON + :members: + :member-order: bysource + +Grover's Algorithm +"""""""""""""""""" .. autoclass:: qibo.models.grover.Grover :members: diff --git a/examples/benchmarks/results/ADIABATIC.md b/examples/benchmarks/results/ADIABATIC.md index c0f6b3d0a9..5e2aaab060 100644 --- a/examples/benchmarks/results/ADIABATIC.md +++ b/examples/benchmarks/results/ADIABATIC.md @@ -1,4 +1,4 @@ -# Adiabatic evolution +# Adiabatic evolution Benchmark We use Qibo to simulate adiabatic evolution under the transverse field Ising Hamiltonians using linear scaling `s(t) = t`. We simulate for a total time diff --git a/examples/benchmarks/results/QFT.md b/examples/benchmarks/results/QFT.md index 0b2950251e..1dbc7f9a89 100644 --- a/examples/benchmarks/results/QFT.md +++ b/examples/benchmarks/results/QFT.md @@ -1,4 +1,4 @@ -# Quantum Fourier Transform +# Quantum Fourier Transform Benchmark The Quantum Fourier Transform is used as a subroutine in many quantum algorithms and thus constitutes an example with great practical importance. @@ -7,7 +7,7 @@ all of which are available in Qibo and other used libraries, except QCGPU where `SWAP` was implemented using three `CNOT` gates. -## Quantum Fourier Transform (`complex64`) +## Single precision (`complex64`) `nqubits` | Qibo (GPU) | Qibo (multi-GPU) | QCGPU (GPU) | Qibo (CPU) | Qibo (CPU-1) | QCGPU (CPU) | Cirq (CPU) | TFQ (CPU) -- | -- | -- | -- | -- | -- | -- | -- | -- @@ -25,7 +25,7 @@ except QCGPU where `SWAP` was implemented using three `CNOT` gates. ![qft-64](../images/qft_c64.png) -## Quantum Fourier Transform (`complex128`) +## Double precision (`complex128`) `nqubits` | Qibo (GPU) | Qibo (multi-GPU) | Qulacs (GPU) | Qibo (CPU) | Qibo (CPU-1) | Qulacs (CPU) | IntelQS (CPU) | Qiskit (CPU) | PyQuil (CPU) -- | -- | -- | -- | -- | -- | -- | -- | -- | -- diff --git a/examples/benchmarks/results/SHOTS.md b/examples/benchmarks/results/SHOTS.md index 7b9529b998..12a4943307 100644 --- a/examples/benchmarks/results/SHOTS.md +++ b/examples/benchmarks/results/SHOTS.md @@ -1,4 +1,4 @@ -# Measurement shots +# Measurement Shots Benchmark Qibo simulates quantum measurements using its standard dense state vector simulator, followed by sampling from the distribution corresponding to the final diff --git a/examples/benchmarks/results/VAR5.md b/examples/benchmarks/results/VAR5.md index b8a0d9ffca..e97cdc8060 100644 --- a/examples/benchmarks/results/VAR5.md +++ b/examples/benchmarks/results/VAR5.md @@ -1,4 +1,4 @@ -# Variational circuit +# Variational Circuit Benchmark Variational circuits are typically used in quantum machine learning and similar applications and constitute a good candidate for applications of near-term @@ -13,7 +13,7 @@ The configuration is repeated for five layers and the variational parameters are chosen randomly from 0 to 2pi in all benchmarks. -## Variational circuit (`complex64`) +## Single precision (`complex64`) `nqubits` | Qibo (V100) | QCGPU (V100) | Qibo (CPU) | QCGPU (CPU) | Cirq (CPU) | TFQ (CPU) -- | -- | -- | -- | -- | -- | -- @@ -25,13 +25,13 @@ are chosen randomly from 0 to 2pi in all benchmarks. 30 | 3.21179 | 13.82931 | 42.34363 | 133.33737 | 3674.24012 | 454.36985 31 | 5.8706 | 28.10267 | 86.44963 | 272.46449 | 7477.32876 | core dumped 32 | OOM | OOM | 179.30808 | fails | 14950.43044 | -33 | | | 366.64664 | | fails | -34 | | | 759.29275 | | | +33 | | | 366.64664 | | fails | +34 | | | 759.29275 | | | ![var5-c64](../images/var5layer_c64.png) -## Variational circuit (`complex128`) +## Double precision (`complex128`) `nqubits` | Qibo (V100) | Qulacs (V100) | Qibo (CPU) | Qulacs (CPU) | IntelQS (CPU) | Qiskit (CPU) | PyQuil (CPU) -- | -- | -- | -- | -- | -- | -- | -- @@ -41,8 +41,8 @@ are chosen randomly from 0 to 2pi in all benchmarks. 28 | 1.6831 | 8.10374 | 14.84471 | 54.18735 | 242.76708 | 2146.48768 | 869.36062 29 | 3.04162 | 16.42699 | 30.48203 | 110.05296 | 525.83829 | 4275.40255 | fails 30 | 5.827 | 33.46775 | 62.2646 | 225.94718 | 1043.19987 | 9324.27692 | -31 | OOM | OOM | 128.6027 | 460.50558 | 2100.28082 | fails | -32 | | | 263.47171 | 947.05816 | 4365.85043 | | -33 | | | 544.67465 | fails | 8946.80753 | | +31 | OOM | OOM | 128.6027 | 460.50558 | 2100.28082 | fails | +32 | | | 263.47171 | 947.05816 | 4365.85043 | | +33 | | | 544.67465 | fails | 8946.80753 | | ![var5-c128](../images/var5layer_c128.png)