IterativeAmplitudeEstimation#

class qiskit.algorithms.IterativeAmplitudeEstimation(epsilon_target, alpha, confint_method='beta', min_ratio=2, quantum_instance=None, sampler=None)[소스]#

기반 클래스: AmplitudeEstimator

The Iterative Amplitude Estimation algorithm.

This class implements the Iterative Quantum Amplitude Estimation (IQAE) algorithm, proposed in [1]. The output of the algorithm is an estimate that, with at least probability \(1 - \alpha\), differs by epsilon to the target value, where both alpha and epsilon can be specified.

It differs from the original QAE algorithm proposed by Brassard [2] in that it does not rely on Quantum Phase Estimation, but is only based on Grover’s algorithm. IQAE iteratively applies carefully selected Grover iterations to find an estimate for the target amplitude.

참조

[1]: Grinko, D., Gacon, J., Zoufal, C., & Woerner, S. (2019).

Iterative Quantum Amplitude Estimation. arXiv:1912.05559.

[2]: Brassard, G., Hoyer, P., Mosca, M., & Tapp, A. (2000).

Quantum Amplitude Amplification and Estimation. arXiv:quant-ph/0005055.

The output of the algorithm is an estimate for the amplitude a, that with at least probability 1 - alpha has an error of epsilon. The number of A operator calls scales linearly in 1/epsilon (up to a logarithmic factor).

버전 0.24.0부터 폐지됨: qiskit.algorithms.amplitude_estimators.iae.IterativeAmplitudeEstimation.__init__()’s argument quantum_instance 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 sampler argument. See https://qisk.it/algo_migration for a migration guide.

매개변수:

  • epsilon_target (float) – Target precision for estimation target a, has values between 0 and 0.5

  • alpha (float) – Confidence level, the target probability is 1 - alpha, has values between 0 and 1

  • confint_method (str) – Statistical method used to estimate the confidence intervals in each iteration, can be ‘chernoff’ for the Chernoff intervals or ‘beta’ for the Clopper-Pearson intervals (default)

  • min_ratio (float) – Minimal q-ratio (\(K_{i+1} / K_i\)) for FindNextK

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

  • sampler (BaseSampler | None) – A sampler primitive to evaluate the circuits.

예외 발생:

  • AlgorithmError – if the method to compute the confidence intervals is not supported

  • ValueError – If the target epsilon is not in (0, 0.5]

  • ValueError – If alpha is not in (0, 1)

  • ValueError – If confint_method is not supported

Attributes

epsilon_target#

Returns the target precision epsilon_target of the algorithm.

반환:

The target precision (which is half the width of the confidence interval).

quantum_instance#

Deprecated. Get the quantum instance.

버전 0.24.0부터 폐지됨: The property qiskit.algorithms.amplitude_estimators.iae.IterativeAmplitudeEstimation.quantum_instance is deprecated as of qiskit-terra 0.24.0. It will be removed no earlier than 3 months after the release date. See https://qisk.it/algo_migration for a migration guide.

반환:

The quantum instance used to run this algorithm.

sampler#

Get the sampler primitive.

반환:

The sampler primitive to evaluate the circuits.

Methods

construct_circuit(estimation_problem, k=0, measurement=False)[소스]#

Construct the circuit \(\mathcal{Q}^k \mathcal{A} |0\rangle\).

The A operator is the unitary specifying the QAE problem and Q the associated Grover operator.

매개변수:

  • estimation_problem (EstimationProblem) – The estimation problem for which to construct the QAE circuit.

  • k (int) – The power of the Q operator.

  • measurement (bool) – Boolean flag to indicate if measurements should be included in the circuits.

반환:

The circuit implementing \(\mathcal{Q}^k \mathcal{A} |0\rangle\).

반환 형식:

QuantumCircuit

estimate(estimation_problem)[소스]#

Run the amplitude estimation algorithm on provided estimation problem.

매개변수:

estimation_problem (EstimationProblem) – The estimation problem.

반환:

An amplitude estimation results object.

예외 발생:
반환 형식:

IterativeAmplitudeEstimationResult