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schemes.py
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from pennylane import numpy as np
import time
from sklearn.metrics import accuracy_score, f1_score
from ChemModel import translator, quantum_net
from Arguments import Arguments
import random
import pickle
import csv, os
import torch.multiprocessing as mp
import pennylane as qml
from math import pi
def net2str(net):
net_str = ''
for i in range(len(net)):
net_str += str(net[i])
return net_str
def get_time(f):
def inner(*arg,**kwarg):
s_time = time.time()
res = f(*arg,**kwarg)
e_time = time.time()
print('Time：{}'.format(round((e_time - s_time), 2)))
return res
return inner
@get_time
def chemistry(hamiltonian, design, net, rate = 100, verbose=None):
seeds = [20, 21, 30, 33, 36, 42, 43, 55, 67, 170]
args = Arguments()
lr = args.qlr
symbols = ["O", "H"]
coordinates = np.array([[0.0, 0.0, 0.0], [0.45, -0.1525, -0.8454]])
# Building the molecular hamiltonian for the trihydrogen cation
# hamiltonian, qubits = qml.qchem.molecular_hamiltonian(symbols, coordinates, charge=1)
dev = qml.device("lightning.qubit", wires=args.n_qubits)
@qml.qnode(dev, diff_method="adjoint")
def cost_fn(theta):
quantum_net(theta, design)
return qml.expval(hamiltonian)
energy = []
for i in range(1):
np.random.seed(seeds[5]) #42
if verbose: print('seed:', seeds[i])
q_params = 2 * pi * np.random.rand(design['layer_repe'] * args.n_qubits * 2)
opt = qml.GradientDescentOptimizer(stepsize = lr)
# opt = qml.AdamOptimizer(stepsize=0.01, beta1=0.9, beta2=0.99, eps=1e-08)
for n in range(rate):
q_params, prev_energy = opt.step_and_cost(cost_fn, q_params)
if verbose: print(f"--- Step: {n}, Energy: {cost_fn(q_params):.8f}")
energy.append(cost_fn(q_params))
metrics = np.mean(energy)
report = {'energy': metrics}
print(metrics)
# filename = 'models/' + net
# with open(filename, 'wb') as file:
# pickle.dump([report, q_params], file)
# with open('NoiseModel/fakekolkata.pkl', 'rb') as file:
# noise_model = pickle.load(file)
# import qiskit_aer.noise as noise
# noise_model1 = noise.NoiseModel()
# noise_modelreal = noise_model1.from_dict(noise_model)
# dev = qml.device('qiskit.aer', wires=args.n_qubits, noise_model=noise_modelreal)
# @qml.qnode(dev)
# def cost_noise(theta):
# quantum_net(theta, design)
# return qml.expval(hamiltonian)
# print("Noise:", cost_noise(q_params))
return report
def mask(net, positions = None):
if positions:
single = positions[0]
double = positions[1]
for i in single:
net[i] = 'n'
for i in double:
net[12 + i] = 'n'
else:
mask = [random.sample(range(1, 12), 3), random.sample(range(12, 24), 10)]
rz = mask[1][-1]
for i in range(2):
for j in range(len(mask[i])):
net[mask[i][j]] = 'n'
net[rz] = rz - 12
return net
def search(hamiltonian, train_space, index, size):
filename = 'train_results_{}.csv'.format(index)
if os.path.isfile(filename) == False:
with open(filename, 'w+', newline='') as res:
writer = csv.writer(res)
writer.writerow(['Num', 'sample_id', 'arch_code', 'Energy'])
csv_reader = csv.reader(open(filename))
i = len(list(csv_reader)) - 1
j = index * size + i
while len(train_space) > 0:
net = train_space[i]
net = mask(net)
print('Net', j, ":", net)
design = translator(net)
report = chemistry(hamiltonian, design, net2str(net))
with open(filename, 'a+', newline='') as res:
writer = csv.writer(res)
metrics = report['energy']
writer.writerow([i, j, net, metrics])
j += 1
i += 1
def run(net, rate=None):
with open('data/OHhamiltonian', 'rb') as outfile:
hamiltonian = pickle.load(outfile)
design = translator(net)
net = net2str(net)
report = chemistry(hamiltonian, design, net, rate)
if __name__ == '__main__':
with open('data/OHhamiltonian', 'rb') as outfile:
hamiltonian = pickle.load(outfile)
train_space = []
filename = 'data/train_space_1'
with open(filename, 'rb') as file:
train_space = pickle.load(file)
model_path = 'models'
if os.path.exists(model_path) == False:
os.makedirs(model_path)
num_processes = 10
size = int(len(train_space) / num_processes)
space = []
for i in range(num_processes):
space.append(train_space[i*size : (i+1)*size])
with mp.Pool(processes = num_processes) as pool:
pool.starmap(search, [(hamiltonian, space[i], i, size) for i in range(num_processes)])
# net = [0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 3, 7, 6, 3, 1, 1, 10, 6, 5, 6, 6, 7]
# mask = [random.sample(range(1, 12), 6), random.sample(range(12, 24), 6)]
# for i in range(2):
# for j in range(len(mask[i])):
# net[mask[i][j]] = 'n'
# print(net)
# net = [0, 0, 0, 0, 1, 'n', 0, 1, 'n', 'n', 0, 'n', 'n', 'n', 6, 3, 'n', 1, 'n', 7, 'n', 6, 'n', 7]
# design = translator(net)
# net = net2str(net)
# report = chemistry(hamiltonian, design, net, 'print')
# with open('models/'+ net, 'rb') as file:
# a = pickle.load(file)