mindquantum.framework.MQEncoderOnlyOps

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

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

Parameters: expectation_with_grad (GradOpsWrapper) – a grad ops that receive encoder data and ansatz data and return the 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.

Outputs:

Tensor, The expectation value of the hamiltonian.

Supported Platforms:

GPU, CPU

Examples

>>> import numpy as np
>>> from mindquantum import Circuit, Hamiltonian, QubitOperator
>>> from mindquantum import Simulator, MQEncoderOnlyOps
>>> import mindspore as ms
>>> ms.context.set_context(mode=ms.context.PYNATIVE_MODE, device_target="CPU")
>>> circ = Circuit().ry('a', 0).h(0).rx('b', 0)
>>> ham = Hamiltonian(QubitOperator('Z0'))
>>> sim = Simulator('projectq', 1)
>>> grad_ops = sim.get_expectation_with_grad(ham, circ, encoder_params_name=circ.params_name)
>>> data = np.array([[0.1, 0.2], [0.3, 0.4]])
>>> f, g = grad_ops(data)
>>> f
array([[0.0978434 +0.j],
       [0.27219214+0.j]])
>>> net = MQEncoderOnlyOps(grad_ops)
>>> f_ms = net(ms.Tensor(data))
>>> f_ms
Tensor(shape=[2, 1], dtype=Float32, value=
[[ 9.78433937e-02],
 [ 2.72192121e-01]])