class QAOA(optimizer=None, reps=1, initial_state=None, mixer=None, initial_point=None, gradient=None, expectation=None, include_custom=False, max_evals_grouped=1, callback=None, quantum_instance=None)[소스]

기반 클래스: VQE

Deprecated: Quantum Approximate Optimization Algorithm.

The QAOA class has been superseded by the qiskit.algorithms.minimum_eigensolvers.QAOA class. This class will be deprecated in a future release and subsequently removed after that.

QAOA is a well-known algorithm for finding approximate solutions to combinatorial-optimization problems.

The QAOA implementation directly extends VQE and inherits VQE’s optimization structure. However, unlike VQE, which can be configured with arbitrary ansatzes, QAOA uses its own fine-tuned ansatz, which comprises \(p\) parameterized global \(x\) rotations and \(p\) different parameterizations of the problem hamiltonian. QAOA is thus principally configured by the single integer parameter, p, which dictates the depth of the ansatz, and thus affects the approximation quality.

An optional array of \(2p\) parameter values, as the initial_point, may be provided as the starting beta and gamma parameters (as identically named in the original QAOA paper) for the QAOA ansatz.

An operator or a parameterized quantum circuit may optionally also be provided as a custom mixer Hamiltonian. This allows, as discussed in this paper for quantum annealing, and in this paper for QAOA, to run constrained optimization problems where the mixer constrains the evolution to a feasible subspace of the full Hilbert space.

버전 0.24.0부터 폐지됨: The class qiskit.algorithms.minimum_eigen_solvers.qaoa.QAOA is deprecated as of qiskit-terra 0.24.0. It will be removed no earlier than 3 months after the release date. Instead, use the class qiskit.algorithms.minimum_eigensolvers.QAOA. See https://qisk.it/algo_migration for a migration guide.

  • optimizer (Optimizer | Minimizer | None) – A classical optimizer, see also VQE for more details on the possible types.

  • reps (int) – the integer parameter \(p\) as specified in https://arxiv.org/abs/1411.4028, Has a minimum valid value of 1.

  • initial_state (QuantumCircuit | None) – An optional initial state to prepend the QAOA circuit with

  • mixer (QuantumCircuit | OperatorBase) – the mixer Hamiltonian to evolve with or a custom quantum circuit. Allows support of optimizations in constrained subspaces as per https://arxiv.org/abs/1709.03489 as well as warm-starting the optimization as introduced in http://arxiv.org/abs/2009.10095.

  • initial_point (np.ndarray | None) – An optional initial point (i.e. initial parameter values) for the optimizer. If None then it will simply compute a random one.

  • gradient (GradientBase | Callable[[np.ndarray | list], list] | None) – An optional gradient operator respectively a gradient function used for optimization.

  • expectation (ExpectationBase | None) – The Expectation converter for taking the average value of the Observable over the ansatz state function. When None (the default) an ExpectationFactory is used to select an appropriate expectation based on the operator and backend. When using Aer qasm_simulator backend, with paulis, it is however much faster to leverage custom Aer function for the computation but, although VQE performs much faster with it, the outcome is ideal, with no shot noise, like using a state vector simulator. If you are just looking for the quickest performance when choosing Aer qasm_simulator and the lack of shot noise is not an issue then set include_custom parameter here to True (defaults to False).

  • include_custom (bool) – When expectation parameter here is None setting this to True will allow the factory to include the custom Aer pauli expectation.

  • max_evals_grouped (int) – Max number of evaluations performed simultaneously. Signals the given optimizer that more than one set of parameters can be supplied so that potentially the expectation values can be computed in parallel. Typically this is possible when a finite difference gradient is used by the optimizer such that multiple points to compute the gradient can be passed and if computed in parallel improve overall execution time. Ignored if a gradient operator or function is given.

  • callback (Callable[[int, np.ndarray, float, float], None] | None) – a callback that can access the intermediate data during the optimization. Four parameter values are passed to the callback as follows during each evaluation by the optimizer for its current set of parameters as it works towards the minimum. These are: the evaluation count, the optimizer parameters for the ansatz, the evaluated mean and the evaluated standard deviation.

  • quantum_instance (QuantumInstance | Backend | None) – Quantum Instance or Backend



Computes minimum eigenvalue.


Return the circuits used to compute the expectation value.


Generate the ansatz circuit and expectation value measurement, and return their runnable composition.


Returns a function handle to evaluates the energy at given parameters for the ansatz.


Preparing the setting of VQE into a string.


Whether computing the expectation value of auxiliary operators is supported.



Returns the ansatz.


Returns callback


The expectation value algorithm used to construct the expectation measurement from the observable.


Returns the gradient.


Returns include_custom


Returns initial point


Returns: Returns the initial state.


Returns max_evals_grouped


Returns: Returns the mixer.


Returns optimizer


Returns quantum instance.


Prepare the setting of VQE as a string.