Optimizers (qiskit_algorithms.optimizers)

Classical Optimizers.

This package contains a variety of classical optimizers and were designed for use by qiskit_algorithm’s 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 Classes

OptimizerResult	The result of an optimization routine.
Optimizer	Base class for optimization algorithm.
Minimizer	Callable Protocol for minimizer.

Steppable Optimization

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 scikit-quant. 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, hence it has been made an optional install. 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 the optional dependent NLOpt package is installed. To install the NLOpt dependent package you can use pip install nlopt.

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.