mindquantum.framework.MQN2Ops

class mindquantum.framework.MQN2Ops(expectation_with_grad)[source]

MindQuantum operator that get the square of absolute value of expectation of a hamiltonian on a quantum state evaluated by a parameterized quantum circuit (PQC). This PQC should contains a encoder circuit and an ansatz circuit. This ops is PYNATIVE_MODE supported only.

\[O = \left|\left<\varphi\right| U^\dagger_l H U_r\left|\psi\right>\right|^2\]

Parameters: expectation_with_grad (GradOpsWrapper) – a grad ops that receive encoder data and ansatz data and return the square of absolute value of expectation value and gradient value of parameters respect to expectation.

Inputs:

enc_data (Tensor) - Tensor of encoder data with shape \((N, M)\) that you want to encode into quantum state, where \(N\) means the batch size and \(M\) means the number of encoder parameters.
ans_data (Tensor) - Tensor with shape \(N\) for ansatz circuit, where \(N\) means the number of ansatz parameters.

Outputs:

Tensor, The square of absolute value of expectation value of the hamiltonian.

Supported Platforms:

GPU, CPU

Examples

>>> import numpy as np
>>> from mindquantum import Circuit, Hamiltonian, QubitOperator
>>> from mindquantum import Simulator, MQN2Ops
>>> import mindspore as ms
>>> ms.context.set_context(mode=ms.context.PYNATIVE_MODE, device_target="CPU")
>>> enc = Circuit().ry('a', 0)
>>> ans = Circuit().h(0).rx('b', 0)
>>> ham = Hamiltonian(QubitOperator('Z0'))
>>> sim = Simulator('projectq', 1)
>>> grad_ops = sim.get_expectation_with_grad(ham, enc+ans,
...                                          encoder_params_name=['a'],
...                                          ansatz_params_name=['b'])
>>> enc_data = np.array([[0.1]])
>>> ans_data = np.array([0.2])
>>> f, g_enc, g_ans = grad_ops(enc_data, ans_data)
>>> np.abs(f) ** 2
array([[0.00957333]])
>>> net = MQN2Ops(grad_ops)
>>> f_ms = net(ms.Tensor(enc_data), ms.Tensor(ans_data))
>>> f_ms
Tensor(shape=[1, 1], dtype=Float32, value=
[[ 9.57333017e-03]])