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- # Copyright 2021 Huawei Technologies Co., Ltd
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
- # ============================================================================
- """Benchmark for QAOA with PaddlePaddle Quantum"""
- import time
- import numpy as np
- import paddle
- from paddle_quantum.circuit import UAnsatz
-
- n = 12
- V = range(n)
- E = [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 0), (0, 3), (1, 4), (2, 6),
- (6, 7), (7, 8), (3, 8), (3, 9), (4, 9), (0, 10), (10, 11), (3, 11)]
-
- h_d_list = []
- for (u, v) in E:
- h_d_list.append([-1.0, 'z' + str(u) + ',z' + str(v)])
-
-
- def circuit_qaoa(p, gamma, beta):
- cir = UAnsatz(n)
- cir.superposition_layer()
- for layer in range(p):
- for (u, v) in E:
- cir.cnot([u, v])
- cir.rz(gamma[layer], v)
- cir.cnot([u, v])
-
- for v in V:
- cir.rx(beta[layer], v)
-
- return cir
-
-
- class Net(paddle.nn.Layer):
- def __init__(self, p, dtype="float64"):
- super(Net, self).__init__()
-
- self.p = p
- self.gamma = self.create_parameter(
- shape=[self.p],
- default_initializer=paddle.nn.initializer.Uniform(low=0.0,
- high=2 * np.pi),
- dtype=dtype,
- is_bias=False)
- self.beta = self.create_parameter(
- shape=[self.p],
- default_initializer=paddle.nn.initializer.Uniform(low=0.0,
- high=2 * np.pi),
- dtype=dtype,
- is_bias=False)
-
- def forward(self):
- cir = circuit_qaoa(self.p, self.gamma, self.beta)
- cir.run_state_vector()
- loss = -cir.expecval(h_d_list)
-
- return loss, cir
-
-
- p = 4
- ITR = 120
- LR = 0.1
- SEED = 1024
-
- paddle.seed(SEED)
-
- net = Net(p)
- opt = paddle.optimizer.Adam(learning_rate=LR, parameters=net.parameters())
- t0 = time.time()
- for itr in range(1, ITR + 1):
- loss, cir = net()
- loss.backward()
- opt.minimize(loss)
- opt.clear_grad()
- if itr % 10 == 0:
- print("iter:", itr, " loss:", "%.4f" % loss.numpy())
- t1 = time.time()
- print('Total time for paddle quantum :{}'.format(t1 - t0))
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