-
Notifications
You must be signed in to change notification settings - Fork 1
/
reg_pca_vqc.py
176 lines (144 loc) · 6.75 KB
/
reg_pca_vqc.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Mar 31 13:05:48 2022
@author: junqi
"""
import h5py
import argparse
import pennylane as qml
from pennylane import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from tc.tc_fc import TTLinear
from sklearn.decomposition import PCA
parser = argparse.ArgumentParser(description='Training a TTN-VQC model on the MNIST dataset')
parser.add_argument('--save_path', metavar='DIR', default='models', help='saved model path')
parser.add_argument('--num_qubits', default=8, help='The number of qubits', type=int)
parser.add_argument('--batch_size', default=50, help='the batch size', type=int)
parser.add_argument('--num_epochs', default=15, help='The number of epochs', type=int)
parser.add_argument('--depth_vqc', default=6, help='The depth of VQC', type=int)
parser.add_argument('--lr', default=0.005, help='Learning rate', type=float)
parser.add_argument('--feat_dims', default=784, help='The dimensions of features', type=int)
parser.add_argument('--n_class', default=8, help='number of classification classes', type=int)
parser.add_argument('--max_data', default=60000, help='The maximum number of training data', type=int)
args = parser.parse_args()
dev = qml.device("default.qubit", wires=args.num_qubits)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
def RY_Layer(w):
"""
1-qubit Pauli-Y rotation gate.
"""
for idx, elem in enumerate(w):
qml.RY(elem, wires=idx)
def RX_Layer(w):
"""
1-qubit Pauli-X rotation gate.
"""
for idx, elem in enumerate(w):
qml.RX(elem, wires=idx)
def RZ_Layer(w):
"""
1-qubit Pauli-Z rotation gate.
"""
for idx, elem in enumerate(w):
qml.RZ(elem, wires=idx)
def Entangle_Layer(n_qubits):
"""
A layer of CNOT gates.
"""
for n_qubit in range(n_qubits):
qml.CNOT(wires=[n_qubit, (n_qubit+1) % n_qubits])
@qml.qnode(dev, interface="torch")
def Quantum_Net(q_input_features, q_weight_flat, q_depth=6, n_qubits=16):
"""
Variational Quantum Circuit (VQC)
"""
# Reshape the weights
q_weights = q_weight_flat.reshape(3, q_depth, n_qubits)
# Embed features in the quantum node
RY_Layer(q_input_features)
# The VQC setup
for k in range(q_depth):
Entangle_Layer(n_qubits)
RX_Layer(q_weights[0, k, :])
RY_Layer(q_weights[1, k, :])
RZ_Layer(q_weights[2, k, :])
# Expectation values of the Pauli-Z operator
exp_vals = [qml.expval(qml.PauliZ(position)) for position in range(n_qubits)]
return tuple(exp_vals)
class TTN_VQC(nn.Module):
def __init__(self, input_dims, n_class, n_qubits, q_depth):
super(TTN_VQC, self).__init__()
self.n_qubits = n_qubits
self.q_depth = q_depth
# self.ttn = TTLinear([7, 16, 7], [2, 2, 2], tt_rank=[1, 3, 3, 1])
self.q_params = nn.Parameter(0.01 * torch.randn(q_depth * n_qubits * 3))
self.post_net = nn.Linear(n_qubits, args.feat_dims)
def forward(self, input_features):
#pre_out = self.ttn(input_features).to(device)
#q_in = pre_out * np.pi / 2.0
q_in = input_features * np.pi / 2.0
# Apply the quantum circuit to each element of the batch and append it to q_out
q_out = torch.Tensor(0, self.n_qubits)
q_out = q_out.to(device)
for elem in q_in:
q_out_elem = Quantum_Net(elem, self.q_params, self.q_depth, self.n_qubits).float().unsqueeze(0)
q_out = torch.cat((q_out.to(device), q_out_elem.to(device)))
return self.post_net(q_out)
if __name__ == "__main__":
model = TTN_VQC(args.feat_dims, args.n_class, args.num_qubits, args.depth_vqc).to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
# Data preparation
data_file = h5py.File('dataset/data_sub_noise_mnist.h5', 'r')
train_normal_data = data_file['tr_data3_normal'][:args.max_data][:]
train_clean_data = data_file['tr_data3'][:args.max_data][:]
test_normal_data = data_file['te_data3_normal'][:args.max_data][:]
test_clean_data = data_file['te_data3'][:args.max_data][:]
test_laplace_data = data_file['te_data3_laplace'][:args.max_data][:]
pca = PCA(n_components=8)
train_normal_data = pca.fit_transform(train_normal_data)
# train_clean_data = pca.fit_transform(train_clean_data)
test_normal_data = pca.fit_transform(test_normal_data)
test_laplace_data = pca.fit_transform(test_laplace_data)
# test_clean_data = pca.fit_transform(test_clean_data)
n_batches = int(args.max_data / args.batch_size)
# Model training
model.train()
for epoch in range(1, args.num_epochs+1):
train_loss = 0
for i in range(n_batches):
data = torch.from_numpy(train_normal_data[i*args.batch_size:(1+i)*args.batch_size]).float()
target = torch.from_numpy(train_clean_data[i*args.batch_size:(1+i)*args.batch_size]).long()
optimizer.zero_grad()
output = model(data)
loss = F.l1_loss(output, target)
loss.backward()
optimizer.step()
train_loss += loss.item()
if i % 10 == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, i * len(data), args.max_data,
100. * i / n_batches, loss.item()))
print('Train Epcoh: {} \tLoss: {:.6f}'.format(epoch, train_loss / n_batches))
model.eval()
test_loss_normal = 0
test_loss_laplace = 0
n_batches = int(len(test_normal_data)/args.batch_size)
with torch.no_grad():
for idx in range(n_batches):
data_normal = torch.from_numpy(test_normal_data[idx*args.batch_size:(idx+1)*args.batch_size][:]).float()
data_laplace = torch.from_numpy(test_laplace_data[idx*args.batch_size:(idx+1)*args.batch_size][:]).float()
target = torch.from_numpy(test_clean_data[idx*args.batch_size:(idx+1)*args.batch_size]).long()
output_normal = model(data_normal)
output_laplace = model(data_laplace)
test_loss_normal += F.l1_loss(output_normal, target).item()
test_loss_laplace += F.l1_loss(output_laplace, target).item()
# pred = output.argmax(dim=1, keepdim=True)
# correct += pred.eq(target.view_as(pred)).sum().item()
test_loss_normal /= args.batch_size
test_loss_laplace /= args.batch_size
# acc = 100. * float(correct) / len(test_data)
print('\nTest set: Average loss: normal {:.4f}, laplace ({:.4f})\n'.format(
test_loss_normal, test_loss_laplace))