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 near-term quantum devices with short depth circuits.
Algorithms configuration includes the use of components 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. Aqua ultimately uses Terra(opens in a new tab) for the actual compilation and execution of the quantum circuits created by the algorithm and its components.
Algorithms Base Class
QuantumAlgorithm | Base class for Quantum Algorithms. |
ClassicalAlgorithm | 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 | The Grover’s Search algorithm. |
Amplitude Estimators
Algorithms that estimate a value.
AmplitudeEstimation | The Quantum Phase Estimation-based Amplitude Estimation algorithm. |
IterativeAmplitudeEstimation | The Iterative Amplitude Estimation algorithm. |
MaximumLikelihoodAmplitudeEstimation | The Maximum Likelihood Amplitude Estimation algorithm. |
Classifiers
Algorithms for data classification.
QSVM | Quantum SVM algorithm. |
VQC | The Variational Quantum Classifier algorithm. |
SklearnSVM | The Sklearn SVM algorithm (classical). |
Distribution Learners
QGAN | 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.
BernsteinVazirani | The Bernstein-Vazirani algorithm. |
DeutschJozsa | The Deutsch-Jozsa algorithm. |
EOH | The Quantum EOH (Evolution of Hamiltonian) algorithm. |
Simon | 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.
NumPyEigensolver | The NumPy Eigensolver algorithm. |
Factorizers
Algorithms to find factors of a number.
Shor | Shor’s factoring algorithm. |
Linear Solvers
Algorithms to find solutions for linear equations of equations.
HHL | The HHL algorithm. |
NumPyLSsolver | The Numpy LinearSystem algorithm (classical). |
Minimum Eigensolvers
Algorithms that can find the minimum eigenvalue of an operator.
Note: The ClassicalCPLEX algorithm requires IBM ILOG CPLEX Optimization Studio(opens in a new tab) and its Python API to be installed. See the following for more information:
MinimumEigensolver | The Minimum Eigensolver Interface. |
MinimumEigensolverResult | Minimum Eigensolver Result. |
ClassicalCPLEX | The Classical CPLEX algorithm (classical). |
IQPE | The Iterative Quantum Phase Estimation algorithm. |
NumPyMinimumEigensolver | The Numpy Minimum Eigensolver algorithm. |
QAOA | The Quantum Approximate Optimization Algorithm. |
QPE | The Quantum Phase Estimation algorithm. |
VQE | The Variational Quantum Eigensolver algorithm. |