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Documentation fixes and updates (qiskit-community#36)
Co-authored-by: Elena Peña Tapia <[email protected]> Co-authored-by: Julien Gacon <[email protected]>
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‎docs/apidocs/qiskit_algorithms.AlgorithmError.rst

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AlgorithmError
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==============
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.. currentmodule:: qiskit_algorithms
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.. autoexception:: AlgorithmError

‎docs/apidocs/qiskit_algorithms.gradients.rst

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‎docs/apidocs/qiskit_algorithms.optimizers.rst

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‎docs/apidocs/qiskit_algorithms.state_fidelities.rst

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‎docs/apidocs/qiskit_algorithms.time_evolvers.trotterization.rst

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‎docs/apidocs/qiskit_algorithms.time_evolvers.variational.rst

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‎docs/index.rst

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Overview <self>
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Getting Started <getting_started>
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API References <apidocs/algorithms>
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API References <apidocs/qiskit_algorithms>
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Tutorials <tutorials/index>
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Release Notes <release_notes>
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GitHub <https://github.com/qiskit-community/qiskit-algorithms>

‎qiskit_algorithms/__init__.py

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# that they have been altered from the originals.
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"""
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=====================================
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Algorithms (:mod:`qiskit_algorithms`)
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=====================================
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It contains a collection of quantum algorithms, for use with quantum computers, to
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carry out research and investigate how to solve problems in different domains on
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near-term quantum devices with short depth circuits.
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Algorithms configuration includes the use of :mod:`~qiskit_algorithms.optimizers` which
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were designed to be swappable sub-parts of an algorithm. Any component and may be exchanged for
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a different implementation of the same component type in order to potentially alter the behavior
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and outcome of the algorithm.
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============================================
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Qiskit Algorithms (:mod:`qiskit_algorithms`)
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============================================
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Qiskit Algorithms is a library of quantum algorithms for quantum computing with
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`Qiskit <https://qiskit.org>`_.
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These algorithms can be used to carry out research and investigate how to solve
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problems in different domains on simulators and near-term real quantum devices
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using shallow circuits.
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The library includes some algorithms, for example the :class:`.NumPyMinimumEigensolver`, which take
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the same input as their quantum counterpart but solve the problem classically. This has utility in
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the near-term, where problems are still tractable classically, to validate and/or act as a reference.
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There are also classical :mod:`.optimizers` for use with variational algorithms such as :class:`.VQE`.
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This package also provides common building blocks for algorithms, such quantum circuit
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gradients (:mod:`.gradients`) and fidelities of quantum states (:mod:`.state_fidelities`).
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These elements are frequently used in a variety of applications, such as variational optimization,
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time evolution and quantum machine learning.
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The quantum algorithms here all use
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`Primitives <https://qiskit.org/documentation/apidoc/primitives.html>`__
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to execute quantum circuits. This can be an
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``Estimator``, which computes expectation values, or a ``Sampler`` which computes
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probability distributions. Refer to the specific algorithm for more information in this regard.
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.. currentmodule:: qiskit_algorithms
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Algorithms
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==========
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It contains a variety of quantum algorithms and these have been grouped by logical function such
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as minimum eigensolvers and amplitude amplifiers. These algorithms are based on the Qiskit Primitives.
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The algorithms now presented are grouped by logical function, such
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as minimum eigensolvers, amplitude amplifiers, time evolvers etc. Within each group, the
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algorithms conform to an interface that allows them to be used interchangeably
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by different applications. E.g. a Qiskit Nature application may take a minimum
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eigensolver to solve a ground state problem, and require it to
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conform to the :class:`.MinimumEigensolver` interface. Any algorithm that conforms to
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the interface, for example :class:`.VQE`, can be used by this application.
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Amplitude Amplifiers
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--------------------
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Algorithms based on amplitude amplification.
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.. autosummary::
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:toctree: ../stubs/
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Amplitude Estimators
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--------------------
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Algorithms based on amplitude estimation.
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.. autosummary::
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:toctree: ../stubs/
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Eigensolvers
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------------
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Algorithms to find eigenvalues of an operator. For chemistry these can be used to find excited
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states of a molecule, and ``qiskit-nature`` has some algorithms that leverage chemistry specific
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knowledge to do this in that application domain.
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These algorithms are based on the Qiskit Primitives.
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.. autosummary::
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:toctree: ../stubs/
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eigensolvers
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Time Evolvers
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-------------
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Algorithms to evolve quantum states in time. Both real and imaginary time evolution is possible
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with algorithms that support them. For machine learning, Quantum Imaginary Time Evolution might be
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used to train Quantum Boltzmann Machine Neural Networks for example.
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These algorithms are based on the Qiskit Primitives.
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.. autosummary::
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:toctree: ../stubs/
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:nosignatures:
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RealTimeEvolver
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ImaginaryTimeEvolver
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TimeEvolutionResult
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TimeEvolutionProblem
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PVQD
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PVQDResult
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SciPyImaginaryEvolver
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SciPyRealEvolver
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VarQITE
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VarQRTE
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Variational Quantum Time Evolution
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++++++++++++++++++++++++++++++++++
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Classes used by variational quantum time evolution algorithms - :class:`.VarQITE` and
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:class:`.VarQRTE`.
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.. autosummary::
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:toctree: ../stubs/
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time_evolvers.variational
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Eigensolver
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EigensolverResult
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NumPyEigensolver
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NumPyEigensolverResult
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VQD
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VQDResult
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Trotterization-based Quantum Real Time Evolution
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++++++++++++++++++++++++++++++++++++++++++++++++
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Package for primitives-enabled Trotterization-based quantum time evolution
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algorithm - :class:`~.time_evolvers.TrotterQRTE`.
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Gradients
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---------
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Algorithms to calculate the gradient of a quantum circuit.
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.. autosummary::
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:toctree: ../stubs/
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:toctree:
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time_evolvers.trotterization
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gradients
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Gradients
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----------
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Minimum Eigensolvers
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--------------------
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Algorithms to find the minimum eigenvalue of an operator.
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Algorithms to calculate the gradient of a quantum circuit. These algorithms are based
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on the Qiskit Primitives.
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This set of these algorithms take an ``Estimator`` primitive and can
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solve for a general Hamiltonian.
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.. autosummary::
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:toctree: ../stubs/
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:nosignatures:
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gradients
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Minimum Eigensolvers
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---------------------
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MinimumEigensolver
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MinimumEigensolverResult
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NumPyMinimumEigensolver
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NumPyMinimumEigensolverResult
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VQE
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VQEResult
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AdaptVQE
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AdaptVQEResult
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Algorithms that can find the minimum eigenvalue of an operator. These algorithms are
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based on the Qiskit Primitives.
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This set of algorithms take a ``Sampler`` primitive and can only
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solve for a diagonal Hamiltonian, such as an Ising Hamiltonian of an optimization problem.
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.. autosummary::
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:toctree: ../stubs/
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:nosignatures:
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minimum_eigensolvers
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SamplingMinimumEigensolver
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SamplingMinimumEigensolverResult
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SamplingVQE
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SamplingVQEResult
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QAOA
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Optimizers
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----------
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Classical optimizers for use by quantum variational algorithms. These algorithms
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are based on the Qiskit Primitives.
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Classical optimizers designed for use by quantum variational algorithms.
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.. autosummary::
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:toctree: ../stubs/
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:toctree:
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optimizers
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Phase Estimators
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----------------
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Algorithms that estimate the phases of eigenstates of a unitary. These algorithms
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are based on the Qiskit Primitives.
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Algorithms that estimate the phases of eigenstates of a unitary.
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.. autosummary::
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State Fidelities
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----------------
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Algorithms that compute the fidelity of pairs of quantum states.
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Algorithms that compute the fidelity of pairs of quantum states. These algorithms
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are based on the Qiskit Primitives.
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.. autosummary::
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:toctree:
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state_fidelities
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Time Evolvers
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-------------
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Algorithms to evolve quantum states in time. Both real and imaginary time evolution is possible
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with algorithms that support them. For machine learning, Quantum Imaginary Time Evolution might be
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used to train Quantum Boltzmann Machine Neural Networks for example.
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.. autosummary::
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:toctree: ../stubs/
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:nosignatures:
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state_fidelities
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RealTimeEvolver
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ImaginaryTimeEvolver
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TimeEvolutionResult
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TimeEvolutionProblem
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PVQD
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PVQDResult
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SciPyImaginaryEvolver
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SciPyRealEvolver
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VarQITE
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VarQRTE
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Variational Quantum Time Evolution
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++++++++++++++++++++++++++++++++++
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Classes used by variational quantum time evolution algorithms -
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:class:`.VarQITE` and :class:`.VarQRTE`.
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.. autosummary::
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:toctree:
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time_evolvers.variational
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Trotterization-based Quantum Real Time Evolution
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++++++++++++++++++++++++++++++++++++++++++++++++
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Trotterization-based quantum time evolution algorithms -
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:class:`~.time_evolvers.trotterization.TrotterQRTE`.
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.. autosummary::
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:toctree:
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time_evolvers.trotterization
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Miscellaneous
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=============
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Various classes used by qiskit-algorithms that are part of and exposed
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by the public API.
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Exceptions
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----------
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.. autosummary::
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:toctree: ../stubs/
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:toctree:
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:nosignatures:
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AlgorithmError
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.. autosummary::
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:toctree: ../stubs/
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:nosignatures:
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AlgorithmJob
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‎qiskit_algorithms/eigensolvers/__init__.py

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# copyright notice, and modified files need to carry a notice indicating
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# that they have been altered from the originals.
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"""
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=====================================================================
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Eigensolvers Package (:mod:`qiskit_algorithms.eigensolvers`)
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=====================================================================
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.. currentmodule:: qiskit_algorithms.eigensolvers
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Eigensolvers
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================
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.. autosummary::
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:toctree: ../stubs/
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Eigensolver
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NumPyEigensolver
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VQD
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Results
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=======
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.. autosummary::
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:toctree: ../stubs/
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EigensolverResult
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NumPyEigensolverResult
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VQDResult
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"""
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"""The Eigensolvers package"""
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from .numpy_eigensolver import NumPyEigensolver, NumPyEigensolverResult
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from .eigensolver import Eigensolver, EigensolverResult

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