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 = {| ⟨ φ | U_{l}^{†} H U_{r} | ψ ⟩ |}^{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]])