Optimization algorithms (qiskit_optimization.algorithms
)#
Algorithms for optimization problems.
Base classes for algorithms and results#
An abstract class for optimization algorithms in Qiskit optimization module. |
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An abstract class that implements multi start optimization and should be sub-classed by other optimizers. |
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A base class for optimization results. |
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A base abstract class for aggregates results |
Algorithms and results#
ADMMOptimization Result. |
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An implementation of the ADMM-based heuristic. |
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Defines a set of parameters for ADMM optimizer. |
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Internal computation state of the ADMM implementation. |
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The SciPy COBYLA optimizer wrapped as an Qiskit |
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The CPLEX optimizer wrapped as an Qiskit |
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Goemans-Williamson algorithm to approximate the max-cut of a problem. |
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Contains results of the Goemans-Williamson algorithm. |
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A result object for Grover Optimization methods. |
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Uses Grover Adaptive Search (GAS) to find the minimum of a QUBO function. |
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The Gurobi optimizer wrapped as an Qiskit |
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Defines whether the intermediate results of |
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Aggregates the results by averaging the probability of each sample. |
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Minimum Eigen Optimizer Result. |
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A wrapper for minimum eigen solvers. |
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Termination status of an optimization algorithm. |
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Recursive Eigen Optimizer Result. |
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A meta-algorithm that applies a recursive optimization. |
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The MILP optimizer from Scipy wrapped as a Qiskit |
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SLSQP optimization result, defines additional properties that may be returned by the optimizer. |
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The SciPy SLSQP optimizer wrapped as an Qiskit |
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A sample of an optimization solution. |
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A meta-algorithm that uses a pre-solver to solve a relaxed version of the problem. |
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A factory that produces quantum circuits for the QAOA implementation. |
Submodules#
Quantum Random Access Optimization (qiskit_optimization.algorithms.qrao) |