Algorithms (qiskit.aqua.algorithms
)¶
Aqua 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 nearterm quantum devices with short depth circuits.
Algorithms configuration includes the use of components
which
were designed to be swappable subparts 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. Aqua ultimately uses
Terra for the actual compilation and execution of the quantum
circuits created by the algorithm and its components.
Algorithms Base Class¶
Base class for Quantum Algorithms. 

Base class for Classical Algorithms. 
Algorithms¶
Aqua contains a variety of quantum algorithms and these have been grouped by logical function such as minimum eigensolvers and amplitude amplifiers.
Additionally Aqua includes some classical algorithms. While these algorithms do not use a quantum device or simulator, and rely on purely classical approaches, they may be useful in the near term to generate reference values while experimenting with, developing and testing quantum algorithms.
The classical algorithms are designed to take the same input data as the quantum algorithms so that behavior, data validity and output can be evaluated and compared to a quantum result.
Amplitude Amplifiers¶
Grover’s Search algorithm. 

Grover Result. 
Amplitude Estimators¶
Algorithms that estimate a value.
AmplitudeEstimationAlgorithm Result. 

The Quantum Phase Estimationbased Amplitude Estimation algorithm. 

AmplitudeEstimation Result. 

The Iterative Amplitude Estimation algorithm. 

IterativeAmplitudeEstimation Result. 

The Maximum Likelihood Amplitude Estimation algorithm. 

MaximumLikelihoodAmplitudeEstimation Result. 
Classifiers¶
Algorithms for data classification.
Quantum SVM algorithm. 

The Variational Quantum Classifier algorithm. 

The Sklearn SVM algorithm (classical). 
Distribution Learners¶
The Quantum Generative Adversarial Network algorithm. 
Education¶
Algorithms whose main role is educational. These are provided as Aqua algorithms so they can be run in the same framework but their existence here is principally for educational reasons.
The BernsteinVazirani algorithm. 

The DeutschJozsa algorithm. 

The Quantum EOH (Evolution of Hamiltonian) algorithm. 

The Simon algorithm. 
Eigensolvers¶
Algorithms to find eigenvalues of an operator. For chemistry these can be used to find excited states of a molecule and qiskit.chemistry has some algorithms that leverage chemistry specific knowledge to do this in that application domain.
The Eigensolver Interface. 

Eigensolver Result. 
The NumPy Eigensolver algorithm. 
Factorizers¶
Algorithms to find factors of a number.
Shor’s factoring algorithm. 
Linear Solvers¶
Algorithms to find solutions for linear equations of equations.
Linear solver Result. 

The HHL algorithm. 

HHL Result. 

The Numpy LinearSystem algorithm (classical). 

Numpy LinearSystem Result. 
Minimum Eigensolvers¶
Algorithms that can find the minimum eigenvalue of an operator.
Note: The ClassicalCPLEX
algorithm requires IBM ILOG CPLEX Optimization Studio
and its Python API to be installed. See the following for more information:
The Minimum Eigensolver Interface. 

Minimum Eigensolver Result. 
The Classical CPLEX algorithm (classical). 

The Iterative Quantum Phase Estimation algorithm. 

The Numpy Minimum Eigensolver algorithm. 

The Quantum Approximate Optimization Algorithm. 

The Quantum Phase Estimation algorithm. 

The Variational Quantum Eigensolver algorithm. 