Create test.py

test_and_documents/test.py

+import math
+import numpy as np
+import paddle
+from paddle import matmul, transpose, trace
+from paddle_quantum.circuit import UAnsatz
+from paddle_quantum.utils import dagger, random_pauli_str_generator, pauli_str_to_matrix
+from paddle_quantum.state import vec, vec_random, density_op, density_op_random
+theta_size = 4
+num_qubits = 2
+ITR = 80
+LR=0.5
+SEED = 1
+paddle.seed(SEED)
+path = '/home/aistudio/飞桨常规赛：量子电路合成/Question_2_Unitary.txt'
+
+
+#普通构建方法
+def U_theta(theta,num_qubits):
+    cir = UAnsatz(num_qubits)
+    cir.ry(theta[0],0)
+    cir.ry(theta[1],1)
+    cir.cnot([0,1])
+    cir.ry(theta[2],0)
+    cir.ry(theta[3],1)
+    return cir.U
+
+#通过量子比特扩展
+def U_theta2(theta,num_qubits):
+    cir = UAnsatz(1)
+    cir.ry(theta[0],0)
+    cir.expand(num_qubits)
+    cir.ry(theta[1],1)
+    cir.cnot([0,1])
+    cir.ry(theta[2],0)
+    cir.ry(theta[3],1)
+    return cir.U
+
+class Optimization_exl(paddle.nn.Layer):
+    def __init__(self,shape,dtype='float64'):
+        super(Optimization_exl,self).__init__()
+        f = np.loadtxt(path)
+        self.u = paddle.to_tensor(f)
+        self.theta = self.create_parameter(shape=shape,
+                                            default_initializer = paddle.nn.initializer.Uniform(low=0,high=2*np.pi),
+                                            dtype=dtype,is_bias=False)
+    
+    def forward(self):
+        #U = U_theta(self.theta,num_qubits)
+        U = U_theta2(self.theta,num_qubits)
+        U_dagger = dagger(U)
+        loss = 1-(0.25*paddle.real(trace(matmul(self.u,U_dagger)))[0])
+        return loss
+    
+    def result(self):
+        return self.theta
+
+loss_list = []
+parameter_list = []
+myLayer = Optimization_exl([theta_size])
+opt = paddle.optimizer.Adam(learning_rate=LR,parameters=myLayer.parameters())
+
+for itr in range(ITR):
+    loss = myLayer()[0]
+    loss.backward()
+    opt.minimize(loss)
+    opt.clear_grad()
+
+    loss_list.append(loss.numpy()[0])
+    parameter_list.append(myLayer.parameters()[0].numpy())
+    #if itr % 5 == 0:
+        #print('iter:', itr, '  loss: %.4f' % loss.numpy())
+print(myLayer.result())
+"""
+U_theta 结果
+Tensor(shape=[4], dtype=float64, place=CPUPlace, stop_gradient=False,
+       [-0.43797367,  7.05269038,  3.48305136,  0.42135362])
+U_theta2 结果
+Tensor(shape=[4], dtype=float64, place=CPUPlace, stop_gradient=False,
+       [-0.43797367,  7.05269038,  3.48305136,  0.42135362])
+"""