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+# Sphinx build info version 1
+# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
+config: a854d428f49551f4fff5b7464ff1c0e2
+tags: 645f666f9bcd5a90fca523b33c5a78b7
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diff --git a/_sources/apidocs/qiskit_machine_learning.algorithms.rst.txt b/_sources/apidocs/qiskit_machine_learning.algorithms.rst.txt
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+.. _qiskit-machine-learning-algorithms:
+
+.. automodule:: qiskit_machine_learning.algorithms
+   :no-members:
+   :no-inherited-members:
+   :no-special-members:
diff --git a/_sources/apidocs/qiskit_machine_learning.circuit.library.rst.txt b/_sources/apidocs/qiskit_machine_learning.circuit.library.rst.txt
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+.. _qiskit-machine-learning-circuit-library:
+
+.. automodule:: qiskit_machine_learning.circuit.library
+   :no-members:
+   :no-inherited-members:
+   :no-special-members:
diff --git a/_sources/apidocs/qiskit_machine_learning.connectors.rst.txt b/_sources/apidocs/qiskit_machine_learning.connectors.rst.txt
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+.. _qiskit-machine-learning-connectors:
+
+.. automodule:: qiskit_machine_learning.connectors
+   :no-members:
+   :no-inherited-members:
+   :no-special-members:
diff --git a/_sources/apidocs/qiskit_machine_learning.datasets.rst.txt b/_sources/apidocs/qiskit_machine_learning.datasets.rst.txt
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+.. _qiskit-machine-learning-datasets:
+
+.. automodule:: qiskit_machine_learning.datasets
+   :no-members:
+   :no-inherited-members:
+   :no-special-members:
diff --git a/_sources/apidocs/qiskit_machine_learning.kernels.algorithms.rst.txt b/_sources/apidocs/qiskit_machine_learning.kernels.algorithms.rst.txt
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+.. _qiskit-machine-learning-kernels-algorithms:
+
+.. automodule:: qiskit_machine_learning.kernels.algorithms
+   :no-members:
+   :no-inherited-members:
+   :no-special-members:
diff --git a/_sources/apidocs/qiskit_machine_learning.kernels.rst.txt b/_sources/apidocs/qiskit_machine_learning.kernels.rst.txt
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+.. _qiskit-machine-learning-kernels:
+
+.. automodule:: qiskit_machine_learning.kernels
+   :no-members:
+   :no-inherited-members:
+   :no-special-members:
diff --git a/_sources/apidocs/qiskit_machine_learning.neural_networks.rst.txt b/_sources/apidocs/qiskit_machine_learning.neural_networks.rst.txt
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+.. _qiskit-machine-learning-neural_networks:
+
+.. automodule:: qiskit_machine_learning.neural_networks
+   :no-members:
+   :no-inherited-members:
+   :no-special-members:
diff --git a/_sources/apidocs/qiskit_machine_learning.rst.txt b/_sources/apidocs/qiskit_machine_learning.rst.txt
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+=====================================
+Qiskit Machine Learning API Reference
+=====================================
+
+.. _qiskit-machine-learning:
+
+.. automodule:: qiskit_machine_learning
+   :no-members:
+   :no-inherited-members:
+   :no-special-members:
diff --git a/_sources/apidocs/qiskit_machine_learning.utils.loss_functions.rst.txt b/_sources/apidocs/qiskit_machine_learning.utils.loss_functions.rst.txt
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+.. _qiskit-machine-learning-utils-loss_functions:
+
+.. automodule:: qiskit_machine_learning.utils.loss_functions
+   :no-members:
+   :no-inherited-members:
+   :no-special-members:
diff --git a/_sources/apidocs/qiskit_machine_learning.utils.rst.txt b/_sources/apidocs/qiskit_machine_learning.utils.rst.txt
@@ -0,0 +1,6 @@
+.. _qiskit-machine-learning-utils:
+
+.. automodule:: qiskit_machine_learning.utils
+   :no-members:
+   :no-inherited-members:
+   :no-special-members:
diff --git a/_sources/getting_started.rst.txt b/_sources/getting_started.rst.txt
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+:orphan:
+
+###############
+Getting started
+###############
+
+Installation
+============
+
+Qiskit Machine Learning depends on Qiskit, which has its own
+`Qiskit Getting Started <https://qiskit.org/documentation/getting_started.html>`__ detailing
+installation options and its supported environments/platforms. You should refer to
+that first. Then the information here can be followed which focuses on the additional installation
+specific to Qiskit Machine Learning.
+
+Qiskit Machine Learning has some functions that have been made optional where the dependent code and/or
+support program(s) are not (or cannot be) installed by default. Those are PyTorch and Sparse.
+See :ref:`optional_installs` for more information.
+
+.. tab-set::
+
+    .. tab-item:: Start locally
+
+        The simplest way to get started is to first follow the `getting started 'Start locally' guide for
+        Qiskit <https://qiskit.org/documentation/getting_started.html>`__
+
+        In your virtual environment, where you installed Qiskit, install Qiskit Machine Learning as follows:
+
+        .. code:: sh
+
+            pip install qiskit-machine-learning
+
+        .. note::
+
+            As Qiskit Machine Learning depends on Qiskit, you can though simply install it into your
+            environment, as above, and pip will automatically install a compatible version of Qiskit
+            if one is not already installed.
+
+    .. tab-item:: Install from source
+
+       Installing Qiskit Machine Learning from source allows you to access the most recently
+       updated version under development instead of using the version in the Python Package
+       Index (PyPI) repository. This will give you the ability to inspect and extend
+       the latest version of the Qiskit Machine Learning code more efficiently.
+
+       Since Qiskit Machine Learning depends on Qiskit, and its latest changes may require new or changed
+       features of Qiskit, you should first follow Qiskit's `"Install from source"` instructions
+       here `Qiskit Getting Started <https://qiskit.org/documentation/getting_started.html>`__
+
+       .. raw:: html
+
+          <h2>Installing Qiskit Machine Learning from Source</h2>
+
+       Using the same development environment that you installed Qiskit in you are ready to install
+       Qiskit Machine Learning.
+
+       1. Clone the Qiskit Machine Learning repository.
+
+          .. code:: sh
+
+             git clone https://github.com/qiskit-community/qiskit-machine-learning.git
+
+       2. Cloning the repository creates a local folder called ``qiskit-machine-learning``.
+
+          .. code:: sh
+
+             cd qiskit-machine-learning
+
+       3. If you want to run tests or linting checks, install the developer requirements.
+
+          .. code:: sh
+
+             pip install -r requirements-dev.txt
+
+       4. Install ``qiskit-machine-learning``.
+
+          .. code:: sh
+
+             pip install .
+
+       If you want to install it in editable mode, meaning that code changes to the
+       project don't require a reinstall to be applied, you can do this with:
+
+       .. code:: sh
+
+          pip install -e .
+
+
+.. _optional_installs:
+
+Optional installs
+=================
+
+* **PyTorch**, may be installed either using command ``pip install 'qiskit-machine-learning[torch]'`` to install the
+  package or refer to PyTorch `getting started <https://pytorch.org/get-started/locally/>`__. When PyTorch
+  is installed, the `TorchConnector` facilitates its use of quantum computed networks.
+
+* **Sparse**, may be installed using command ``pip install 'qiskit-machine-learning[sparse]'`` to install the
+  package. Sparse being installed will enable the usage of sparse arrays/tensors.
+
+----
+
+Ready to get going?...
+======================
+
+.. raw:: html
+
+   <div class="tutorials-callout-container">
+      <div class="row">
+
+.. qiskit-call-to-action-item::
+   :description: Find out about Qiskit Machine Learning.
+   :header: Dive into the tutorials
+   :button_link:  ./tutorials/index.html
+   :button_text: Qiskit Machine Learning tutorials
+
+.. raw:: html
+
+      </div>
+   </div>
+
+
+.. Hiding - Indices and tables
+   :ref:`genindex`
+   :ref:`modindex`
+   :ref:`search`
diff --git a/_sources/index.rst.txt b/_sources/index.rst.txt
@@ -0,0 +1,89 @@
+#####################################
+Qiskit Machine Learning overview
+#####################################
+
+Overview
+==============
+
+Qiskit Machine Learning introduces fundamental computational building blocks - such as Quantum Kernels
+and Quantum Neural Networks - used in different applications, including classification and regression.
+On the one hand, this design is very easy to use and allows users to rapidly prototype a first model
+without deep quantum computing knowledge. On the other hand, Qiskit Machine Learning is very flexible,
+and users can easily extend it to support cutting-edge quantum machine learning research.
+
+Qiskit Machine Learning provides the :class:`~qiskit_machine_learning.kernels.FidelityQuantumKernel`
+class class that makes use of the :class:`~qiskit_algorithms.state_fidelities.BaseStateFidelity` algorithm
+introduced in Qiskit and can be easily used to directly compute kernel matrices for given datasets
+or can be passed to a Quantum Support Vector Classifier
+(:class:`~qiskit_machine_learning.algorithms.QSVC`) or
+Quantum Support Vector Regressor (:class:`~qiskit_machine_learning.algorithms.QSVR`)
+to quickly start solving classification or regression problems.
+It also can be used with many other existing kernel-based machine learning algorithms from established
+classical frameworks.
+
+Qiskit Machine Learning defines a generic interface for neural networks that is implemented by different
+quantum neural networks. Two core implementations are readily provided, such as the
+:class:`~qiskit_machine_learning.neural_networks.EstimatorQNN`
+and the :class:`~qiskit_machine_learning.neural_networks.SamplerQNN`.
+The :class:`~qiskit_machine_learning.neural_networks.EstimatorQNN` leverages
+the :class:`~qiskit.primitives.BaseEstimator` primitive from Qiskit and allows users to combine
+parametrized quantum circuits with quantum mechanical observables. The circuits can be constructed
+using, for example, building blocks from Qiskit's circuit library, and the QNN's output is given
+by the expected value of the observable.
+The :class:`~qiskit_machine_learning.neural_networks.SamplerQNN` leverages another primitive
+introduced in Qiskit, the :class:`~qiskit.primitives.BaseSampler` primitive. This neural network
+translates quasi-probabilities of bitstrings estimated by the primitive into a desired output. This
+translation step can be used to interpret a given bitstring in a particular context, e.g.
+translating it into a set of classes.
+
+The neural networks include the functionality to evaluate them for a given input as well as to compute the
+corresponding gradients, which is important for efficient training. To train and use neural networks,
+Qiskit Machine Learning provides a variety of learning algorithms such as the
+:class:`~qiskit_machine_learning.algorithms.NeuralNetworkClassifier` and
+:class:`~qiskit_machine_learning.algorithms.NeuralNetworkRegressor`.
+Both take a QNN as input and then use it in a classification or regression context.
+To allow an easy start, two convenience implementations are provided - the Variational Quantum Classifier
+(:class:`~qiskit_machine_learning.algorithms.VQC`)
+as well as the Variational Quantum Regressor (:class:`~qiskit_machine_learning.algorithms.VQR`).
+Both take just a feature map and an ansatz and construct the underlying QNN automatically.
+
+In addition to the models provided directly in Qiskit Machine Learning, it has the
+:class:`~qiskit_machine_learning.connectors.TorchConnector`,
+which allows users to integrate all of our quantum neural networks directly into the
+`PyTorch <https://pytorch.org>`__
+open source machine learning library. Thanks to Qiskit Algorithm's gradient algorithms,
+this includes automatic
+differentiation - the overall gradients computed by `PyTorch <https://pytorch.org>`__
+during the backpropagation take into
+account quantum neural networks, too. The flexible design also allows the building of connectors
+to other packages in the future.
+
+
+
+Next Steps
+=================================
+
+`Getting started <getting_started.html>`_
+
+`Migration Guide <migration/index.html>`_
+
+`Tutorials <tutorials/index.html>`_
+
+.. toctree::
+    :hidden:
+
+    Overview <self>
+    Getting Started <getting_started>
+    Migration Guide <migration/index>
+    Tutorials <tutorials/index>
+    API Reference <apidocs/qiskit_machine_learning>
+    Release Notes <release_notes>
+    GitHub <https://github.com/qiskit-community/qiskit-machine-learning>
+
+
+
+.. Hiding - Indices and tables
+   :ref:`genindex`
+   :ref:`modindex`
+   :ref:`search`
+