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Algorithms (qiskit.algorithms)

It contains a collection of quantum algorithms, for use with quantum computers, to carry out research and investigate how to solve problems in different domains on near-term quantum devices with short depth circuits.

Algorithms configuration includes the use of optimizers which were designed to be swappable sub-parts of an algorithm. Any component and may be exchanged for a different implementation of the same component type in order to potentially alter the behavior and outcome of the algorithm.

Quantum algorithms are run via a QuantumInstance which must be set with the desired backend where the algorithm's circuits will be executed and be configured with a number of compile and runtime parameters controlling circuit compilation and execution. It ultimately uses Terra for the actual compilation and execution of the quantum circuits created by the algorithm and its components.

Algorithms

It contains a variety of quantum algorithms and these have been grouped by logical function such as minimum eigensolvers and amplitude amplifiers.

Amplitude Amplifiers

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

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.

Eigen Solvers

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. These algorithms are pending deprecation. One should instead make use of the Eigensolver classes in the section below, which leverage Runtime primitives.

Eigensolver

Pending deprecation: Eigensolver Interface.

EigensolverResult

Pending deprecation: Eigensolver Result.

NumPyEigensolver

Pending deprecation: NumPy Eigensolver algorithm.

VQD

Pending deprecation: Variational Quantum Deflation algorithm.

VQDResult

Pending deprecation: VQD Result.

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.

eigensolvers

Eigensolvers Package (qiskit.algorithms.eigensolvers)

Evolvers

Pending deprecation: This package has been superseded by the package below. It will be deprecated in a future release and subsequently removed after that:

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.

RealEvolver

Pending deprecation: Interface for Quantum Real Time Evolution.

ImaginaryEvolver

Pending deprecation: Interface for Quantum Imaginary Time Evolution.

TrotterQRTE

Pending deprecation: Quantum Real Time Evolution using Trotterization.

EvolutionResult

Pending deprecation: Class for holding evolution result.

EvolutionProblem

Pending deprecation: Evolution problem class.

Time Evolvers

Primitives-enabled 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.

PVQD

The projected Variational Quantum Dynamics (p-VQD) Algorithm.

PVQDResult

The result object for the p-VQD algorithm.

TimeEvolutionResult

Class for holding time evolution result.

TimeEvolutionProblem

Time evolution problem class.

Trotterization-based Quantum Real Time Evolution

Package for primitives-enabled Trotterization-based quantum time evolution algorithm - TrotterQRTE.

time_evolvers.trotterization

This package contains Trotterization-based Quantum Real Time Evolution algorithm.

Factorizers

Algorithms to find factors of a number.

Shor

The deprecated Shor's factoring algorithm.

ShorResult

The deprecated Shor Result.

Gradients

Algorithms to calculate the gradient of a quantum circuit.

gradients

Gradients (qiskit.algorithms.gradients)

Linear Solvers

Algorithms to solve linear systems of equations.

linear_solvers

The deprecated Linear solvers (qiskit.algorithms.linear_solvers) It contains classical and quantum algorithms to solve systems of linear equations such as HHL. Although the quantum algorithm accepts a general Hermitian matrix as input, Qiskit's default Hamiltonian evolution is exponential in such cases and therefore the quantum linear solver will not achieve an exponential speedup. Furthermore, the quantum algorithm can find a solution exponentially faster in the size of the system than their classical counterparts (i.e. logarithmic complexity instead of polynomial), meaning that reading the full solution vector would kill such speedup (since this would take linear time in the size of the system). Therefore, to achieve an exponential speedup we can only compute functions from the solution vector (the so called observables) to learn information about the solution. Known efficient implementations of Hamiltonian evolutions or observables are contained in the following subfolders:

Minimum Eigen Solvers

Algorithms that can find the minimum eigenvalue of an operator. These algorithms are pending deprecation. One should instead make use of the Minimum Eigensolver classes in the section below, which leverage Runtime primitives.

MinimumEigensolver

Pending deprecation: Minimum Eigensolver Interface.

MinimumEigensolverResult

Pending deprecation: Minimum Eigensolver Result.

NumPyMinimumEigensolver

Pending deprecation: Numpy Minimum Eigensolver algorithm.

QAOA

Pending deprecation: Quantum Approximate Optimization Algorithm.

VQE

Pending deprecation: Variational Quantum Eigensolver algorithm.

Minimum Eigensolvers

Algorithms that can find the minimum eigenvalue of an operator and leverage primitives.

minimum_eigensolvers

Minimum Eigensolvers Package (qiskit.algorithms.minimum_eigensolvers)

Optimizers

Classical optimizers for use by quantum variational algorithms.

optimizers

Optimizers (qiskit.algorithms.optimizers) It contains a variety of classical optimizers for use by quantum variational algorithms, such as VQE. Logically, these optimizers can be divided into two categories:

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 Fidelity Interfaces (qiskit.algorithms.state_fidelities)

Exceptions

AlgorithmError(*message)

For Algorithm specific errors.

Utility methods

Utility methods used by algorithms.

eval_observables(quantum_instance, ...[, ...])

Pending deprecation: Accepts a list or a dictionary of operators and calculates their expectation values - means and standard deviations.

estimate_observables(estimator, ...[, ...])

Accepts a sequence of operators and calculates their expectation values - means and metadata.

Utility classes

Utility classes used by algorithms (mainly for type-hinting purposes).

AlgorithmJob(function, *args, **kwargs)

This empty class is introduced for typing purposes.