Qiskit Algorithms (qiskit_algorithms)

Qiskit Algorithms is a library of quantum algorithms for quantum computing with Qiskit. These algorithms can be used to carry out research and investigate how to solve problems in different domains on simulators and near-term real quantum devices using shallow circuits.

The library includes some algorithms, for example the NumPyMinimumEigensolver, which take the same input as their quantum counterpart but solve the problem classically. This has utility in the near-term, where problems are still tractable classically, to validate and/or act as a reference. There are also classical optimizers for use with variational algorithms such as VQE.

This package also provides common building blocks for algorithms, such quantum circuit gradients (gradients) and fidelities of quantum states (state_fidelities). These elements are frequently used in a variety of applications, such as variational optimization, time evolution and quantum machine learning.

The quantum algorithms here all use Primitives to execute quantum circuits. This can be an Estimator, which computes expectation values, or a Sampler which computes probability distributions. Refer to the specific algorithm for more information in this regard.

Algorithms

The algorithms now presented are grouped by logical function, such as minimum eigensolvers, amplitude amplifiers, time evolvers etc. Within each group, the algorithms conform to an interface that allows them to be used interchangeably by different applications. E.g. a Qiskit Nature application may take a minimum eigensolver to solve a ground state problem, and require it to conform to the MinimumEigensolver interface. Any algorithm that conforms to the interface, for example VQE, can be used by this application.

Amplitude Amplifiers

Algorithms based on amplitude amplification.

AmplificationProblem	The amplification problem is the input to amplitude amplification algorithms, like Grover.
AmplitudeAmplifier	The interface for amplification algorithms.
Grover	Grover's Search algorithm.
GroverResult	Grover Result.

Amplitude Estimators

Algorithms based on amplitude estimation.

AmplitudeEstimator	The Amplitude Estimation interface.
AmplitudeEstimatorResult	The results object for amplitude estimation algorithms.
AmplitudeEstimation	The Quantum Phase Estimation-based Amplitude Estimation algorithm.
AmplitudeEstimationResult	The AmplitudeEstimation result object.
EstimationProblem	The estimation problem is the input to amplitude estimation algorithm.
FasterAmplitudeEstimation	The Faster Amplitude Estimation algorithm.
FasterAmplitudeEstimationResult	The result object for the Faster Amplitude Estimation algorithm.
IterativeAmplitudeEstimation	The Iterative Amplitude Estimation algorithm.
IterativeAmplitudeEstimationResult	The IterativeAmplitudeEstimation result object.
MaximumLikelihoodAmplitudeEstimation	The Maximum Likelihood Amplitude Estimation algorithm.
MaximumLikelihoodAmplitudeEstimationResult	The MaximumLikelihoodAmplitudeEstimation result object.

Eigensolvers

Algorithms to find eigenvalues of an operator. For chemistry these can be used to find excited states of a molecule, and qiskit-nature has some algorithms that leverage chemistry specific knowledge to do this in that application domain.

Eigensolver	The eigensolver interface.
EigensolverResult	Eigensolver result.
NumPyEigensolver	The NumPy eigensolver algorithm.
NumPyEigensolverResult	NumPy eigensolver result.
VQD	The Variational Quantum Deflation algorithm.
VQDResult	VQD Result.

Gradients

Algorithms to calculate the gradient of a quantum circuit.

gradients

Gradients (qiskit_algorithms.gradients) Algorithms to calculate the gradient of a quantum circuit.

Minimum Eigensolvers

Algorithms to find the minimum eigenvalue of an operator.

This set of these algorithms take an Estimator primitive and can solve for a general Hamiltonian.

MinimumEigensolver	The minimum eigensolver interface.
MinimumEigensolverResult	Minimum eigensolver result.
NumPyMinimumEigensolver	The NumPy minimum eigensolver algorithm.
NumPyMinimumEigensolverResult	NumPy minimum eigensolver result.
VQE	The Variational Quantum Eigensolver (VQE) algorithm.
VQEResult	The Variational Quantum Eigensolver (VQE) result.
AdaptVQE	The Adaptive Variational Quantum Eigensolver algorithm.
AdaptVQEResult	AdaptVQE Result.

This set of algorithms take a Sampler primitive and can only solve for a diagonal Hamiltonian, such as an Ising Hamiltonian of an optimization problem.

SamplingMinimumEigensolver	The Sampling Minimum Eigensolver Interface.
SamplingMinimumEigensolverResult	Sampling Minimum Eigensolver Result.
SamplingVQE	The Variational Quantum Eigensolver algorithm, optimized for diagonal Hamiltonians.
SamplingVQEResult	The SamplingVQE Result.
QAOA	The Quantum Approximate Optimization Algorithm (QAOA).

Optimizers

Classical optimizers designed for use by quantum variational algorithms.

optimizers

Optimizers (qiskit_algorithms.optimizers) Classical Optimizers.

Phase Estimators

Algorithms that estimate the phases of eigenstates of a unitary.

HamiltonianPhaseEstimation	Run the Quantum Phase Estimation algorithm to find the eigenvalues of a Hermitian operator.
HamiltonianPhaseEstimationResult	Store and manipulate results from running HamiltonianPhaseEstimation.
PhaseEstimationScale	Set and use a bound on eigenvalues of a Hermitian operator in order to ensure phases are in the desired range and to convert measured phases into eigenvectors.
PhaseEstimation	Run the Quantum Phase Estimation (QPE) algorithm.
PhaseEstimationResult	Store and manipulate results from running PhaseEstimation.
IterativePhaseEstimation	Run the Iterative quantum phase estimation (QPE) algorithm.

State Fidelities

Algorithms that compute the fidelity of pairs of quantum states.

state_fidelities

State Fidelities (qiskit_algorithms.state_fidelities) Algorithms that compute the fidelity of pairs of quantum states.

Time Evolvers

Algorithms to evolve quantum states in time. Both real and imaginary time evolution is possible with algorithms that support them. For machine learning, Quantum Imaginary Time Evolution might be used to train Quantum Boltzmann Machine Neural Networks for example.

RealTimeEvolver	Interface for Quantum Real Time Evolution.
ImaginaryTimeEvolver	Interface for Quantum Imaginary Time Evolution.
TimeEvolutionResult	Class for holding time evolution result.
TimeEvolutionProblem	Time evolution problem class.
PVQD	The projected Variational Quantum Dynamics (p-VQD) Algorithm.
PVQDResult	The result object for the p-VQD algorithm.
SciPyImaginaryEvolver	Classical Evolver for imaginary time evolution.
SciPyRealEvolver	Classical Evolver for real time evolution.
TrotterQRTE	Quantum Real Time Evolution using Trotterization.
VarQITE	Variational Quantum Imaginary Time Evolution algorithm.
VarQRTE	Variational Quantum Real Time Evolution algorithm.
VarQTEResult	The result object for the variational quantum time evolution algorithms.

Variational Quantum Time Evolution

Classes used by variational quantum time evolution algorithms - VarQITE and VarQRTE.

time_evolvers.variational

Time Evolvers, Variational (qiskit_algorithms.time_evolvers.variational)

Miscellaneous

Various classes used by qiskit-algorithms that are part of and exposed by the public API.

Exceptions

AlgorithmError

For Algorithm specific errors.

Utility classes

Utility classes and function used by algorithms.

AlgorithmJob

This class is introduced for typing purposes and provides no additional function beyond that inherited from its parents.

utils.algorithm_globals

Global properties for algorithms.