-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathtest_qnns.py
194 lines (168 loc) · 7.28 KB
/
test_qnns.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
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
import time
import numpy as np
from metrics import quantum_risk
import torch
import matplotlib.pyplot as plt
from classic_training import train
from data import uniform_random_data, random_unitary_matrix
from qnns.qnn import get_qnn
torch.manual_seed(4241)
np.random.seed(4241)
def plot_loss(losses, num_qbits, num_layers, num_points, r_list, name_addition=''):
if isinstance(losses[0], list):
losses = np.array(losses)
# losses shape is r x layer x epochs
for j in range(len(num_layers)):
num_layer = num_layers[j]
for i in range(len(r_list)):
r = r_list[i]
loss = losses[i, j]
plt.plot(list(range(len(loss))), loss, label=f"r={r}")
plt.legend()
plt.ylim(0.001)
# plt.yscale('log')
# plt.xscale('log')
plt.title(f"Loss for net with {num_qbits} qbits, {num_layer} layers, {num_points} data points")
plt.xlabel("epochs")
plt.ylabel("loss")
plt.savefig(f"./plots/loss_{num_qbits}_qbits_{num_layer}_layers_{num_points}_datapoints{name_addition}.png")
plt.cla()
else:
plt.plot(list(range(len(losses))), losses)
# plt.yscale('log')
# plt.xscale('log')
plt.title(f"Loss for net with {num_qbits} qbits, {num_layers} layers, {num_points} data points, {2**r_list} schmidt rank")
plt.xlabel("epochs")
plt.ylabel("loss")
plt.savefig(f"./plots/loss_{num_qbits}_qbits_{num_layers}_layers_{num_points}_datapoints_{2**r_list}_schmidtrank{name_addition}.png")
plt.cla()
def init(num_layers, num_qbits, schmidt_rank, num_points, num_epochs, lr, qnn_name, opt_name='Adam', device='cpu'):
"""
Tensor training for QNN
"""
starting_time = time.time()
x_qbits = num_qbits
r_qbits = int(np.ceil(np.log2(schmidt_rank))) # dont use all qubits for reference system
x_wires = list(range(num_qbits)) # does not matter which qubits we are using, since we only want the matrix
# construct QNNobject from qnn_name string
qnn = get_qnn(qnn_name, x_wires, num_layers, device='cpu')
X = torch.from_numpy(np.array(uniform_random_data(schmidt_rank, num_points, x_qbits, r_qbits))).to(device)
U = torch.tensor(random_unitary_matrix(x_qbits), device=device)
X = X.reshape((X.shape[0], int(X.shape[1] / U.shape[0]), U.shape[0])).permute(0, 2, 1)
if opt_name.lower() == 'sgd':
optimizer = torch.optim.SGD
else:
optimizer = torch.optim.Adam
if isinstance(qnn.params, list):
optimizer = optimizer(qnn.params, lr=lr)
else:
optimizer = optimizer([qnn.params], lr=lr)
scheduler = None
# scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 2, gamma=0.1)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, factor=0.8, patience=10, min_lr=1e-10, verbose=True)
prep_time = time.time() - starting_time
print(f"\tPreparation with {num_qbits} qubits and {num_layers} layers took {prep_time}s")
starting_time = time.time()
losses = train(X, U, qnn, num_epochs, optimizer, scheduler, device=device)
train_time = time.time() - starting_time
print(f"\trisk = {quantum_risk(U, qnn.get_matrix_V())}")
return train_time, prep_time, losses
def min_loss_over_layer(r_list, min_losses_r, num_layers, num_epochs, qbit, lr, qnn):
for i in range(len(r_list)):
r = r_list[i]
min_losses = min_losses_r[i]
plt.plot(num_layers, min_losses, label=f"r={r}")
plt.legend()
plt.xlabel('number of layers')
plt.ylabel('min loss')
plt.title(f'Minimal loss in {num_epochs} epochs for a {qbit} qubit system')
plt.savefig(f'./plots/minimal_loss_{qbit}_qbits_{num_epochs}_epochs_lr={lr}_{qnn}.png')
plt.cla()
def train_time_over_num_layer(r_list, train_times_r, num_layers, num_epochs, qbit, lr, qnn):
for i in range(len(r_list)):
r = r_list[i]
times = train_times_r[i]
plt.plot(num_layers, times, label=f"r={r}")
plt.legend()
plt.xlabel('number of layers')
plt.ylabel('Time for Training [s]')
plt.title(f'Time for Training for {num_epochs} epochs for a {qbit} qubit system')
plt.savefig(f'./plots/train_time_{qbit}_qbits_{num_epochs}_epochs_lr={lr}_{qnn}.png')
plt.cla()
def plot_runtime_to_schmidt_rank():
# num_layers = [1] + list(range(5, 20, 5))
num_layers = [30]
qbits = [4]
num_epochs = 800
lr = 0.1
# qnns = ['PennylaneQNN', 'OffsetQNN', 'Circuit2QNN', 'Circuit5QNN', 'Circuit6QNN', 'Circuit9QNN']
# qnns = ['Circuit11QNN', 'Circuit12QNN', 'Circuit13QNN', 'Circuit14QNN']
# qnns = ['CudaCircuit6']
# qnns = ['CudaEfficient']
# qnns = ['PennylaneQNN']
qnns = ['CudaPennylane']
# qnns = ['CudaSimpleEnt']
# qnns = ['CudaComplexPennylane']
device = 'cpu'
# device = 'cuda:0'
opt_name = 'Adam'
qnn_losses = []
qnn_times = []
qnn_idx = 0
for qnn in qnns:
qnn_idx += 1
for i in range(len(qbits)):
qbit = qbits[i]
# r_list = [i for i in range(qbit+1)]
r_list = [qbit]
# r_list = [0]
num_points = 2**(qbit)
# num_points = 1
train_times_r = []
prep_times_r = []
min_losses_r = []
losses_r = []
for j in range(len(r_list)):
r = r_list[j]
schmidt_rank = 2**r
min_losses = []
train_times = []
prep_times = []
losses_layer = []
for k in range(len(num_layers)):
num_layer = num_layers[k]
training_time, prep_time, losses = init(num_layer, qbit, schmidt_rank, num_points, num_epochs, lr, qnn, opt_name=opt_name, device=device)
losses_layer.append(losses)
print(f"\tTraining with {qbit} qubits, {num_layer} layers and r={r} took {training_time}s\n")
min_losses.append(np.array(losses).min())
train_times.append(training_time)
prep_times.append(prep_time)
plot_loss(losses, qbit, num_layer, num_points, r, name_addition=f"_{qnn}")
losses_r.append(losses_layer)
min_losses_r.append(min_losses)
train_times_r.append(train_times)
prep_times_r.append(prep_times)
qnn_losses.append((qnn, r, num_points, min_losses))
qnn_times.append((qnn, r, num_points, train_times))
# plot_loss(losses_r, qbit, num_layers, num_points, r_list, name_addition=f"_{num_epochs}_epochs_lr={lr}_{qnn}")
for i in range(len(qnn_losses)):
qnn, r, d, loss = qnn_losses[i]
plt.plot(num_layers, loss, label=f"{qnn}, r={r}, d={d}")
plt.legend()
plt.xlabel('number of layers')
plt.ylabel('min loss')
plt.savefig('./plots/qnn_min_loss.png')
plt.cla()
for i in range(len(qnn_times)):
qnn, r, d, time = qnn_times[i]
plt.plot(num_layers, time, label=f"{qnn}, r={r}, d={d}")
plt.legend()
plt.xlabel('number of layers')
plt.ylabel('time in s')
plt.savefig('./plots/qnn_time.png')
plt.cla()
def main():
plot_runtime_to_schmidt_rank()
# rough_train_time_requirements(0, 0, 0, 4)
if __name__ == '__main__':
main()