使用Tensorflow训练Qiskit电路

为了探索是否有可能用tensorflow训练Qiskit量子电路，我建立了一个小玩具模型。 该玩具模型的目的是通过tensorflow找到正确的角度，以获得独立于输入的“零”输出

import numpy as np
import qiskit
from qiskit.circuit import QuantumCircuit, QuantumRegister
import tensorflow as tf
from tensorflow.keras.layers import Input, Dense, Layer

def QuantumCircuit(thetas, n_qubits=1):
   
   simulator = qiskit.Aer.get_backend('qasm_simulator')
   shots=1024

   circuit= qiskit.QuantumCircuit(n_qubits)
   circuit.h(0)
   circuit.ry(float(thetas),0)
   circuit.measure_all()
   
   job = qiskit.execute(circuit,backend=simulator,shots=shots)
   result = job.result().get_counts(circuit)

   counts = np.array(list(result.values()))
   states = np.array(list(result.keys())).astype(float)

   # Compute probabilities for each state
   probabilities = counts / shots
   # Get state expectation
   expectation = np.sum(states * probabilities)

   return np.array(expectation)

class Linear(Layer):
   def __init__(self,units=1,input_dim=1):
       super(Linear,self).__init__()
       self.w = self.add_weight(shape=(input_dim,units),initializer='random_uniform', trainable=True)        
       
   def call(self, inputs, input_dim=1):
       if (tf.executing_eagerly()):
           return QuantumCircuit(self.w)           
       return inputs

x_train = np.arange(10)
y_train = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])

inputs=Input(shape=(1,))
outputs=Linear()(inputs)
model=tf.keras.models.Model(inputs=inputs,outputs=outputs)
model.summary()
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.01),
loss=tf.keras.losses.MeanSquaredError())
model.fit(x_train, y_train, epochs=100, batch_size=1, verbose=1)

不幸的是，玩具模型不工作，我得到以下错误：

优化器_v2.py:1219_过滤器_梯度 （[v.name代表u，v在grads和vars中]，） ValueError:没有为任何变量提供渐变：['variable:0']

所以我试着“自己”计算梯度：

作为量子电路被调用之前的中间步骤。但这两者都不起作用：-(

是否有人有其他想法将Qiskit电路插入tensorflow，并处理tensorflow的自动微分框架在这种情况下不起作用的事实？ 非常感谢

@tf.custom_gradient
def custom_activation(w):
    result  = QuantumCircuit(w)
    
    def grad(dy):
        eps=0.0001
        result1=QuantumCircuit(w)
        result2=QuantumCircuit(w+eps)
        grad=(result2-result1)/eps
        return dy * [grad]

    return result, grad