Migrating Pytorch backend to Qiboml

doc/source/code-examples/advancedexamples.rst

@@ -543,7 +543,9 @@ refer to the :ref:`Optimizers <Optimizers>` section of the documentation.
 Note that if the Stochastic Gradient Descent optimizer is used then the user
 has to use a backend based on tensorflow or pytorch primitives and not the default custom
 backend, as custom operators currently do not support automatic differentiation.
-To switch the backend one can do ``qibo.set_backend("tensorflow")`` or ``qibo.set_backend("pytorch")``.
+To switch the backend one can do ``qibo.set_backend(backend="qiboml", platform="tensorflow")``
+or ``qibo.set_backend(backend="qiboml", platform="pytorch")``, after ensuring the
+Qiboml package has been installed.
 Check the :ref:`How to use automatic differentiation? <autodiff-example>`
 section for more details.
 
@@ -782,7 +784,7 @@ The following script optimizes the parameters of the variational circuit of the
 .. code-block:: python
 
     import qibo
-    qibo.set_backend("pytorch")
+    qibo.set_backend(backend="qiboml", platform="pytorch")
     import torch
     from qibo import gates, models
 

doc/source/code-examples/examples.rst

@@ -39,9 +39,9 @@ evaluation performance, e.g.:
 .. code-block::  python
 
     import numpy as np
-    # switch backend to "tensorflow"
+    # switch backend to "tensorflow" through the Qiboml provider
     import qibo
-    qibo.set_backend("tensorflow")
+    qibo.set_backend(backend="qiboml", platform="tensorflow")
     from qibo import Circuit, gates
 
     c = Circuit(2)
@@ -54,7 +54,7 @@ evaluation performance, e.g.:
         init_state = np.ones(4) / 2.0 + i
         c(init_state)
 
-Note that compiling is only supported when the native ``tensorflow`` backend is
+Note that compiling is only supported when the ``tensorflow`` backend is
 used. This backend is much slower than ``qibojit`` which uses custom operators
 to apply gates.
 
@@ -226,7 +226,7 @@ For applications that require the state vector to be collapsed during measuremen
 we refer to the :ref:`How to collapse state during measurements? <collapse-examples>`
 
 The measured shots are obtained using pseudo-random number generators of the
-underlying backend (numpy or Tensorflow). If the user has installed a custom
+underlying backend. If the user has installed a custom
 backend (eg. qibojit) and asks for frequencies with more than 100000 shots,
 a custom Metropolis algorithm will be used to obtain the corresponding samples,
 for increase performance. The user can change the threshold for which this

doc/source/getting-started/installation.rst

@@ -7,15 +7,15 @@ 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 | qibojit | tensorflow |
-+==================+======+=========+============+
-| Linux x86        | Yes  | Yes     | Yes        |
-+------------------+------+---------+------------+
-| MacOS >= 10.15   | Yes  | Yes     | Yes        |
-+------------------+------+---------+------------+
-| Windows          | Yes  | Yes     | Yes        |
-+------------------+------+---------+------------+
++------------------+------+---------+-----------+---------+
+| Operating System | qibo | qibojit |Tensorflow | Pytorch |
++==================+======+=========+===========+=========+
+| Linux x86        | Yes  | Yes     | Yes       | Yes     |
++------------------+------+---------+-----------+---------+
+| MacOS >= 10.15   | Yes  | Yes     | Yes       | Yes     |
++------------------+------+---------+-----------+---------+
+| Windows          | Yes  | Yes     | Yes       | Yes     |
++------------------+------+---------+-----------+---------+
 
 .. note::
       All packages are supported for Python >= 3.9.
@@ -148,35 +148,6 @@ Then proceed with the ``qibojit`` installation using ``pip``
 
 _______________________
 
-.. _installing-tensorflow:
-
-tensorflow
-^^^^^^^^^^
-
-If the `TensorFlow <https://www.tensorflow.org>`_ package is installed Qibo
-will detect and provide to the user the possibility to use ``tensorflow``
-backend.
-
-This backend is used by default if ``qibojit`` is not installed, however, if
-needed, in order to switch to the ``tensorflow`` backend please do:
-
-.. code-block::  python
-
-      import qibo
-      qibo.set_backend("tensorflow")
-
-In order to install the package, we recommend the installation using:
-
-.. code-block:: bash
-
-      pip install qibo tensorflow
-
-.. note::
-      TensorFlow can be installed following its `documentation
-      <https://www.tensorflow.org/install>`_.
-
-_______________________
-
 .. _installing-numpy:
 
 numpy
@@ -197,26 +168,45 @@ please do:
 _______________________
 
 
-.. _installing-pytorch:
+.. _installing-qml-backends:
 
-pytorch
-^^^^^^^
+Backends with automatic differentiation support
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+If you need automatic differentiation support, for tracing gradients of your
+quantum algorithm or for building some quantum machine learning routine,
+the right backends for you are those provided by the `Qiboml <https://github.com/qiboteam/qiboml>`__
+package.
 
-If the `PyTorch <https://pytorch.org/>`_ package is installed Qibo
-will detect and provide to the user the possibility to use ``pytorch``
-backend.
+In particular, Qiboml currently support `Pytorch <https://pytorch.org/>`_ and
+`Tensorflow <https://www.tensorflow.org>`_ interfaces, integrating the qibo functionalities
+into these well-known machine learning frameworks. Quantum layers can be constructed
+and added to your Pytorch or Tensorflow models, and trained using any supported
+optimization routine.
 
-In order to switch to the ``pytorch`` backend please do:
+In order to use these quantum machine learning backends please make sure the
+preferred package is installed following `Tensorflow's <https://www.tensorflow.org/install>`_
+or `Pytorch's <https://pytorch.org/get-started/locally/>`_ installation instructions.
+
+To switch to Tensorflow or Pytorch backend please do:
 
 .. code-block::  python
 
       import qibo
-      qibo.set_backend("pytorch")
+      # in case of Tensorflow
+      qibo.set_backend(backend="qiboml", platform="tensorflow")
+      # in case of Pytorch
+      qibo.set_backend(backend="qiboml", platform="pytorch")
 
-In order to install the package, we recommend the installation using:
+In order to start using automatic differentiation tools with Qibo,
+we recommend the installation using:
 
 .. code-block:: bash
 
-      pip install qibo torch
+      pip install qibo qiboml tensorflow
 
-_______________________
+or
+
+.. code-block:: bash
+
+      pip install qibo qiboml torch