`pyqpanda_alg.QFinance.QAE`¶

Module Contents¶

Classes¶

`QAE`	This class provides a framework for original Quantum Amplitude Estimation(QAE) algorithm [1].
`IQAE`	This class provides a framework for Iterative Quantum Amplitude Estimation(IQAE) algorithm [2].

class pyqpanda_alg.QFinance.QAE.QAE(operator_in=None, qnumber: int = 0, res_index: Union[int, List[int]] = -1, epsilon: float = 0.001, target_state: str = '1')¶

This class provides a framework for original Quantum Amplitude Estimation(QAE) algorithm [1].

Parameters

operator_in : callable f(qubits)

Operator/Circuit of the estimated qubits state.

qnumber : int

The number of all qubits used in circuit.

res_index : int, list

The index of the estimated qubit(s).

epsilon : float

Estimated precision, i.e. the minimum error.

target_state : str

Estimated target state.

References

[1] Brassard G, Hoyer P, Mosca M, et al. Quantum amplitude amplification and estimation[J]. Contemporary Mathematics, 2002, 305: 53-74.

run()¶

Run the quantum amplitude estimation algorithm.

Returns: prob : float

A probability value as the amplitude estimation result.
Examples: An example for implementing an amplitude estimation for target state ‘11’ of the following circuit.

          ┌────────────┐
q_0:  |0>─┤RY(1.047198)├ ─■─
          └────────────┘ ┌┴┐
q_1:  |0>─────────────── ┤X├
                         └─┘

>>> import pyqpanda as pq
>>> from pyqpanda_alg.QFinance import QAE
>>> import numpy as np
>>> def create_cir(qlist):
>>>     cir = pq.QCircuit()
>>>     cir << pq.RY(qlist[0], np.pi / 3) << pq.X(qlist[1]).control(qlist[0])
>>>     return cir
>>> W = QAE.QAE(operator_in=create_cir, qnumber=2, epsilon=0.01, res_index=[0, 1], target_state='11').run()
>>> print(W)
0.24294862790338914

class pyqpanda_alg.QFinance.QAE.IQAE(operator_in=None, qnumber: int = 0, res_index: int = -1, epsilon: float = 0.001)¶

This class provides a framework for Iterative Quantum Amplitude Estimation(IQAE) algorithm [2]. Estimated target state is \(|1\rangle\).