def main_tfq():
"""
DQAS with tensorflow quantum engine
unitary learning example
:return:
"""
p = 4
cset = [
cirq.H(cirq.GridQubit(0, 0)),
cirq.H(cirq.GridQubit(1, 0)),
cirq.CNOT(cirq.GridQubit(0, 0), cirq.GridQubit(1, 0)),
cirq.CNOT(cirq.GridQubit(1, 0), cirq.GridQubit(0, 0)),
cirq.X(cirq.GridQubit(0, 0)),
cirq.X(cirq.GridQubit(1, 0)),
]
set_op_pool(cset)
c = len(cset)
stp, nnp, _ = DQAS_search(
GHZ_vag_tfq,
g=double_qubits_initial(),
p=p,
batch=16,
epochs=5,
verbose=False,
parallel_num=0,
nnp_initial_value=np.zeros([p, c]),
structure_opt=tf.keras.optimizers.Adam(learning_rate=0.15),
)
preset = get_preset(stp).numpy()
GHZ_vag_tfq(double_qubits_initial().send(None), nnp, preset, verbose=True)
def main_tn():
"""
DQAS with tensorcircuit engine backend by TensorNetwork
state preparation example
:return:
"""
# multi start may be necessary
ghz_pool = [
("ry", 0),
("ry", 1),
("ry", 2),
("CNOT", 0, 1),
("CNOT", 1, 0),
("CNOT", 0, 2),
("CNOT", 2, 0),
("H", 0),
("H", 1),
("H", 2),
]
set_op_pool(ghz_pool)
c = len(ghz_pool)
p = 4
stp, nnp, _ = DQAS_search(
GHZ_vag,
p=p,
batch=128,
epochs=200,
verbose=True,
parallel_num=0,
nnp_initial_value=np.zeros([p, c]),
structure_opt=tf.keras.optimizers.Adam(learning_rate=0.15),
)
preset = get_preset(stp).numpy()
GHZ_vag(None, nnp, preset, verbose=True)
def main_4():
qft_4 = partial(qft_qem_vag, n=4)
set_op_pool(
[
cirq.I,
cirq.X,
cirq.Y,
cirq.Z,
cirq.H,
cirq.rx(np.pi / 3),
cirq.rx(np.pi * 2.0 / 3),
cirq.rz(np.pi / 3),
cirq.rz(np.pi * 2.0 / 3),
cirq.S,
cirq.T,
]
)
DQAS_search(
qft_4,
nnp_initial_value=tf.zeros([12, 11]),
p=12,
prethermal=0,
batch=32,
verbose=False,
verbose_func=verbose_output,
epochs=6,
)
def main_3():
qft_3 = partial(qft_qem_vag, n=3)
set_op_pool([cirq.X, cirq.Y, cirq.Z, cirq.I, cirq.T, cirq.S])
DQAS_search(
qft_3,
nnp_initial_value=tf.zeros([6, 6]),
p=6,
prethermal=0,
batch=32,
verbose=False,
epochs=30,
)
structure_opt=tf.keras.optimizers.SGD(learning_rate=learning_rate_sch),
)
with open("qaoa_block.result", "wb") as f:
dump([stp.numpy(), nnp.numpy(), h], f)
epochs = np.arange(len(h))
data = np.array([r.loss for r in h])
plt.plot(epochs, data)
plt.xlabel("epoch")
plt.ylabel("objective")
plt.savefig("qaoa_block.pdf")
layer_pool = [Hlayer, rxlayer, rylayer, rzlayer, xxlayer, yylayer, zzlayer]
block_pool = [
Hlayer,
rx_zz_block,
zz_ry_block,
zz_rx_block,
zz_rz_block,
xx_rz_block,
yy_rx_block,
rx_rz_block,
]
set_op_pool(block_pool)
if __name__ == "__main__":
# main_layerwise_encoding()
main_block_encoding()
import sys
sys.path.insert(0, "../")
import numpy as np
import tensorflow as tf
import tensorcircuit as tc
from tensorcircuit.applications.dqas import (
parallel_qaoa_train,
single_generator,
set_op_pool,
)
from tensorcircuit.applications.layers import *
from tensorcircuit.applications.graphdata import get_graph
tc.set_backend("tensorflow")
set_op_pool([Hlayer, rxlayer, rylayer, rzlayer, xxlayer, yylayer, zzlayer])
if __name__ == "__main__":
parallel_qaoa_train(
[0, 6, 1, 6, 1],
single_generator(get_graph("8B")),
tries=4,
cores=2,
batch=1,
epochs=5,
scale=0.8,
)