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.

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.

Primitive-based Eigensolvers

These algorithms are based on the Qiskit Primitives, a new execution paradigm that replaces the use of QuantumInstance in algorithms. To ensure continued support and development, we recommend using the primitive-based Eigensolvers in place of the legacy QuantumInstance-based ones.

eigensolvers

Eigensolvers Package (qiskit.algorithms.eigensolvers)

Legacy Eigensolvers

These algorithms, still based on the QuantumInstance, are superseded by the primitive-based versions in the section above but are still supported for now.

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.

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.

Primitive-based Time Evolvers

These algorithms are based on the Qiskit Primitives, a new execution paradigm that replaces the use of QuantumInstance in algorithms. To ensure continued support and development, we recommend using the primitive-based Time Evolvers in place of the legacy QuantumInstance-based ones.

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.
VarQITE	Variational Quantum Imaginary Time Evolution algorithm.
VarQRTE	Variational Quantum Real Time Evolution algorithm.

Legacy Time Evolvers

These algorithms, still based on the QuantumInstance, are superseded by the primitive-based versions in the section above but are still supported for now.

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.

Variational Quantum Time Evolution

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

time_evolvers.variational

Variational Quantum Time Evolutions (qiskit.algorithms.time_evolvers.variational)

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 Eigensolvers

Algorithms that can find the minimum eigenvalue of an operator.

Primitive-based Minimum Eigensolvers

These algorithms are based on the Qiskit Primitives, a new execution paradigm that replaces the use of QuantumInstance in algorithms. To ensure continued support and development, we recommend using the primitive-based Minimum Eigensolvers in place of the legacy QuantumInstance-based ones.

minimum_eigensolvers

Minimum Eigensolvers Package (qiskit.algorithms.minimum_eigensolvers)

Legacy Minimum Eigensolvers

These algorithms, still based on the QuantumInstance, are superseded by the primitive-based versions in the section above but are still supported for now.

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.

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.