mindquantum.framework.MQEncoderOnlyOps

class mindquantum.framework.MQEncoderOnlyOps(expectation_with_grad)[源代码]

仅包含encoder线路的量子线路演化算子。通过参数化量子线路(PQC)获得对量子态的哈密顿期望。此算子只能在 PYNATIVE_MODE 下执行。

参数:
  • expectation_with_grad (GradOpsWrapper) - 接收encoder数据和ansatz数据,并返回期望值和参数相对于期望的梯度值。

输入:
  • enc_data (Tensor) - 希望编码为量子态的Tensor,其shape为 \((N, M)\) ,其中 \(N\) 表示batch大小, \(M\) 表示encoder数量。

输出:

Tensor,hamiltonian的期望值。

支持平台:

GPU, CPU

样例:

>>> import numpy as np
>>> import mindspore as ms
>>> from mindquantum.core.circuit import Circuit
>>> from mindquantum.core.operators import Hamiltonian, QubitOperator
>>> from mindquantum.framework import MQEncoderOnlyOps
>>> from mindquantum.simulator import Simulator
>>> ms.set_context(mode=ms.PYNATIVE_MODE, device_target="CPU")
>>> circ = Circuit().ry('a', 0).h(0).rx('b', 0).as_encoder()
>>> ham = Hamiltonian(QubitOperator('Z0'))
>>> sim = Simulator('mqvector', 1)
>>> grad_ops = sim.get_expectation_with_grad(ham, circ)
>>> 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]])