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qiskit.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:

Local Optimizers

Given an optimization problem, a local optimizer is a function that attempts to find an optimal value within the neighboring set of a candidate solution.

Global Optimizers

Given an optimization problem, a global optimizer is a function that attempts to find an optimal value among all possible solutions.

Optimizer Base Class

OptimizerResult

The result of an optimization routine.

OptimizerSupportLevel

Support Level enum for features such as bounds, gradient and initial point

Optimizer

Base class for optimization algorithm.

Minimizer

Callable Protocol for minimizer.

Steppable Optimizer Base Class

optimizer_utils

Utils for optimizers

SteppableOptimizer

Base class for a steppable optimizer.

AskData

Base class for return type of ask().

TellData

Base class for argument type of tell().

OptimizerState

Base class representing the state of the optimizer.

Local Optimizers

ADAM

Adam and AMSGRAD optimizers.

AQGD

Analytic Quantum Gradient Descent (AQGD) with Epochs optimizer.

CG

Conjugate Gradient optimizer.

COBYLA

Constrained Optimization By Linear Approximation optimizer.

L_BFGS_B

Limited-memory BFGS Bound optimizer.

GSLS

Gaussian-smoothed Line Search.

GradientDescent

The gradient descent minimization routine.

GradientDescentState

State of GradientDescent.

NELDER_MEAD

Nelder-Mead optimizer.

NFT

Nakanishi-Fujii-Todo algorithm.

P_BFGS

Parallelized Limited-memory BFGS optimizer.

POWELL

Powell optimizer.

SLSQP

Sequential Least SQuares Programming optimizer.

SPSA

Simultaneous Perturbation Stochastic Approximation (SPSA) optimizer.

QNSPSA

The Quantum Natural SPSA (QN-SPSA) optimizer.

TNC

Truncated Newton (TNC) optimizer.

SciPyOptimizer

A general Qiskit Optimizer wrapping scipy.optimize.minimize.

UMDA

Continuous Univariate Marginal Distribution Algorithm (UMDA).

Qiskit also provides the following optimizers, which are built-out using the optimizers from the scikit-quant package. The scikit-quant package is not installed by default but must be explicitly installed, if desired, by the user - the optimizers therein are provided under various licenses so it has been made an optional install for the end user to choose whether to do so or not. To install the scikit-quant dependent package you can use pip install scikit-quant.

BOBYQA

Bound Optimization BY Quadratic Approximation algorithm.

IMFIL

IMplicit FILtering algorithm.

SNOBFIT

Stable Noisy Optimization by Branch and FIT algorithm.

Global Optimizers

The global optimizers here all use NLopt for their core function and can only be used if their dependent NLopt package is manually installed.

CRS

Controlled Random Search (CRS) with local mutation optimizer.

DIRECT_L

DIviding RECTangles Locally-biased optimizer.

DIRECT_L_RAND

DIviding RECTangles Locally-biased Randomized optimizer.

ESCH

ESCH evolutionary optimizer.

ISRES

Improved Stochastic Ranking Evolution Strategy optimizer.