def construct_initial_state(self, **kwargs):
symmetrise = kwargs.get("symmetrise", False)
self.initial_state = construct_initial_state(self.no_routes,
self.no_cars)
if symmetrise:
ancilla_reg = QuantumRegister(1, 'ancilla')
self.initial_state.add_register(ancilla_reg)
self.initial_state.h(ancilla_reg[0])
def main(args=None):
"""[summary]
Args:
raw_args ([type], optional): [description]. Defaults to None.
"""
start = time()
if args == None:
args = parse()
prob_s_s = []
qubo_no = args["no_samples"]
print("__" * 50, "\nQUBO NO: {}\n".format(qubo_no), "__" * 50)
#Load generated qubo_no
with open(
'qubos_{}_car_{}_routes/qubo_{}.pkl'.format(
args["no_cars"], args["no_routes"], qubo_no), 'rb') as f:
qubo, max_coeff, operator, offset, routes = pkl.load(f)
print(operator)
x_s = find_all_ground_states(qubo)
# Visualise
if args["visual"]:
graph = import_map('melbourne.pkl')
visualise_solution(graph, routes)
# Solve QAOA from QUBO with valid solution
no_couplings = count_coupling_terms(operator)
print("Number of couplings: {}".format(no_couplings))
print("Solving with QAOA...")
no_shots = 10000
backend = Aer.get_backend('statevector_simulator')
quantum_instance = QuantumInstance(backend, shots=no_shots)
optimizer_method = "LN_SBPLX"
optimizer = NLOPT_Optimizer(method=optimizer_method)
print("_" * 50, "\n" + optimizer.__class__.__name__)
print("_" * 50)
quantum_instance = QuantumInstance(backend)
prob_s_s = []
initial_state = construct_initial_state(args["no_routes"], args["no_cars"])
mixer = n_qbit_mixer(initial_state)
next_fourier_point, next_fourier_point_B = [0, 0], [
0, 0
] #Not used for p=1 then gets updated for p>1.
for p in range(1, args["p_max"] + 1):
print("p = {}".format(p))
if p == 1:
points = [[0.75,0]] \
# + [[ np.pi*(2*np.random.rand() - 1) for _ in range(2) ] for _ in range(args["no_restarts"])]
draw_circuit = True
else:
penalty = 0.6
points = generate_points(next_fourier_point,
no_perturb=10,
penalty=0.6)
print(points) \
# + generate_points(next_fourier_point_B, 10, penalty)
draw_circuit = False
#empty lists to save following results to choose best result
results = []
exp_vals = []
for r in range(len(points)):
point = points[r]
if np.amax(np.abs(point)) < np.pi / 2:
qaoa_results, optimal_circ = CustomQAOA(
operator,
quantum_instance,
optimizer,
reps=p,
initial_fourier_point=points[r],
initial_state=initial_state,
mixer=mixer,
construct_circ=draw_circuit)
if r == 0:
next_fourier_point = np.array(qaoa_results.optimal_point)
next_fourier_point = QAOAEx.convert_from_fourier_point(
next_fourier_point, 2 * p + 2)
next_fourier_point = QAOAEx.convert_to_fourier_point(
next_fourier_point, 2 * p + 2)
exp_val = qaoa_results.eigenvalue * max_coeff + offset
exp_vals.append(exp_val)
prob_s = 0
for string in x_s:
prob_s += qaoa_results.eigenstate[
string] if string in qaoa_results.eigenstate else 0
results.append((qaoa_results, optimal_circ, prob_s))
print("Point_no: {}, Exp_val: {}, Prob_s: {}".format(
r, exp_val, prob_s))
else:
print(
"Point_no: {}, was skipped because it is outside of bounds"
.format(r))
minim_index = np.argmin(exp_vals)
optimal_qaoa_result, optimal_circ, optimal_prob_s = results[
minim_index]
# if draw_circuit:
# print(optimal_circ.draw())
minim_exp_val = exp_vals[minim_index]
print("Minimum: {}, prob_s: {}".format(minim_exp_val, optimal_prob_s))
prob_s_s.append(optimal_prob_s)
next_fourier_point_B = np.array(optimal_qaoa_result.optimal_point)
print("Optimal_point: {}".format(next_fourier_point_B))
next_fourier_point_B = QAOAEx.convert_from_fourier_point(
next_fourier_point_B, 2 * p + 2)
next_fourier_point_B = QAOAEx.convert_to_fourier_point(
next_fourier_point_B, 2 * p + 2)
print(prob_s_s)
with open(
'results/{}cars{}routes_qubo{}.csv'.format(args["no_cars"],
args["no_routes"],
args["no_samples"]),
'w') as f:
np.savetxt(f, prob_s_s, delimiter=',')
finish = time()
print("Time Taken: {}".format(finish - start))
def main(args=None):
"""[summary]
Args:
raw_args ([type], optional): [description]. Defaults to None.
"""
start = time()
if args == None:
args = parse()
qubo_no = args["no_samples"]
print_to_file("-" * 50)
print_to_file("QUBO_{}".format(qubo_no))
#Load generated qubo_no
with open(
'qubos_{}_car_{}_routes/qubo_{}.pkl'.format(
args["no_cars"], args["no_routes"], qubo_no), 'rb') as f:
qubo, max_coeff, operator, offset, routes = pkl.load(f)
qubo = QuadraticProgram()
qubo.from_ising(operator)
x_s, opt_value, classical_result = find_all_ground_states(qubo)
print_to_file(classical_result)
#Set optimizer method
method = args["method"]
optimizer = NLOPT_Optimizer(method=method, result_message=False)
# optimizer = COBYLA()
backend = Aer.get_backend("statevector_simulator")
quantum_instance = QuantumInstance(backend=backend)
approx_ratios = []
prob_s_s = []
p_max = args["p_max"]
no_routes, no_cars = (args["no_routes"], args["no_cars"])
custom = True
if custom:
initial_state = construct_initial_state(no_routes=no_routes,
no_cars=no_cars)
mixer = n_qbit_mixer(initial_state)
else:
initial_state, mixer = (None, None)
fourier_parametrise = args["fourier"]
print_to_file("-" * 50)
print_to_file(
"Now solving with TQA_QAOA... Fourier Parametrisation: {}".format(
fourier_parametrise))
# maxeval = 125
for p in range(1, p_max + 1):
construct_circ = False
deltas = np.arange(0.45, 0.91, 0.05)
point = np.append([(i + 1) / p for i in range(p)],
[1 - (i + 1) / p for i in range(p)])
points = [delta * point for delta in deltas]
print_to_file("-" * 50)
print_to_file(" " + "p={}".format(p))
if fourier_parametrise:
points = [
convert_to_fourier_point(point, len(point)) for point in points
]
# maxeval *= 2 #Double max_allowed evals for optimizer
# optimizer.set_options(maxeval = maxeval)
optimizer.set_options(maxeval=1000 * p)
qaoa_results, optimal_circ = CustomQAOA(
operator,
quantum_instance,
optimizer,
reps=p,
initial_state=initial_state,
mixer=mixer,
construct_circ=construct_circ,
fourier_parametrise=fourier_parametrise,
list_points=points,
qubo=qubo)
exp_val = qaoa_results.eigenvalue * max_coeff
state_solutions = {
item[0][::-1]: item[1:]
for item in qaoa_results.eigenstate
}
for item in sorted(state_solutions.items(),
key=lambda x: x[1][1],
reverse=True)[0:5]:
print_to_file(item)
prob_s = 0
for string in x_s:
prob_s += state_solutions[string][
1] if string in state_solutions else 0
prob_s /= len(x_s) #normalise
optimal_point = qaoa_results.optimal_point
if fourier_parametrise:
optimal_point = convert_from_fourier_point(optimal_point,
len(optimal_point))
approx_ratio = 1 - np.abs((opt_value - exp_val) / opt_value)
nfev = qaoa_results.cost_function_evals
print_to_file(
" " + "Optimal_point: {}, Nfev: {}".format(optimal_point, nfev))
print_to_file(" " +
"Exp_val: {}, Prob_s: {}, approx_ratio: {}".format(
exp_val, prob_s, approx_ratio))
approx_ratios.append(approx_ratio)
prob_s_s.append(prob_s)
print_to_file("-" * 50)
print_to_file("QAOA terminated")
print_to_file("-" * 50)
print_to_file("Approximation ratios per layer: {}".format(approx_ratios))
print_to_file("Prob_success per layer: {}".format(prob_s_s))
save_results = np.append(approx_ratios, prob_s_s)
if fourier_parametrise:
with open(
'results_{}cars{}routes/TQA_F_{}.csv'.format(
args["no_cars"], args["no_routes"], args["no_samples"]),
'w') as f:
np.savetxt(f, save_results, delimiter=',')
print_to_file(
"Results saved in results_{}cars{}routes/TQA_F_{}.csv".format(
args["no_cars"], args["no_routes"], args["no_samples"]))
else:
with open(
'results_{}cars{}routes/TQA_NF_{}.csv'.format(
args["no_cars"], args["no_routes"], args["no_samples"]),
'w') as f:
np.savetxt(f, save_results, delimiter=',')
print_to_file(
"Results saved in results_{}cars{}routes/TQA_NF_{}.csv".format(
args["no_cars"], args["no_routes"], args["no_samples"]))
finish = time()
print_to_file("Time Taken: {}".format(finish - start))
def main(args = None):
"""[summary]
Args:
raw_args ([type], optional): [description]. Defaults to None.
"""
start = time()
if args == None:
args = parse()
qubo_no = args["no_samples"]
print_to_file("-"*50)
print_to_file("QUBO_{}".format(qubo_no))
#Load generated qubo_no
with open('qubos_{}_car_{}_routes/qubo_{}.pkl'.format(args["no_cars"], args["no_routes"], qubo_no), 'rb') as f:
qubo, max_coeff, operator, offset, routes = pkl.load(f)
qubo = QuadraticProgram()
qubo.from_ising(operator)
x_s, opt_value, classical_result = find_all_ground_states(qubo)
print_to_file(classical_result)
#Set optimizer method
method = args["method"]
optimizer = NLOPT_Optimizer(method = method, result_message=False)
backend = Aer.get_backend("statevector_simulator")
quantum_instance = QuantumInstance(backend = backend)
approx_ratios = []
prob_s_s = []
p_max = args["p_max"]
no_routes, no_cars = (args["no_routes"], args["no_cars"])
custom = True
if custom:
initial_state = construct_initial_state(no_routes = no_routes, no_cars = no_cars)
mixer = n_qbit_mixer(initial_state)
else:
initial_state, mixer = (None, None)
fourier_parametrise = args["fourier"]
print_to_file("-"*50)
print_to_file("Now solving with QAOA... Fourier Parametrisation: {}".format(fourier_parametrise))
for p in range(1, p_max+1):
if p == 1:
points = [[0,0]] + [ np.random.uniform(low = -np.pi/2+0.01, high = np.pi/2-0.01, size = 2*p) for _ in range(2**p)]
next_point = []
else:
penalty = 0.6
points = [next_point_l] + generate_points(next_point, no_perturb=min(2**p-1,10), penalty=penalty)
construct_circ = False
#empty lists to save following results to choose best result
results = []
exp_vals = []
print_to_file("-"*50)
print_to_file(" "+"p={}".format(p))
optimizer.set_options(maxeval = 1000*p)
for r, point in enumerate(points):
qaoa_results, optimal_circ = CustomQAOA(operator,
quantum_instance,
optimizer,
reps = p,
initial_fourier_point= point,
initial_state = initial_state,
mixer = mixer,
construct_circ= construct_circ,
fourier_parametrise = fourier_parametrise,
qubo = qubo
)
if r == 0:
if fourier_parametrise:
next_point_l = np.zeros(shape = 2*p + 2)
next_point_l[0:p] = qaoa_results.optimal_point[0:p]
next_point_l[p+1:2*p+1] = qaoa_results.optimal_point[p:2*p]
else:
next_point_l = interp_point(qaoa_results.optimal_point)
exp_val = qaoa_results.eigenvalue * max_coeff
exp_vals.append(exp_val)
state_solutions = { item[0][::-1]: item[1:] for item in qaoa_results.eigenstate }
for item in sorted(state_solutions.items(), key = lambda x: x[1][1], reverse = True)[0:5]:
print_to_file( item )
prob_s = 0
for string in x_s:
prob_s += state_solutions[string][1] if string in state_solutions else 0
prob_s /= len(x_s) #normalise
results.append((qaoa_results, optimal_circ, prob_s))
print_to_file(" "+"Point_{}, Exp_val: {}, Prob_s: {}".format(r, exp_val, prob_s))
minim_index = np.argmin(exp_vals)
optimal_qaoa_result, optimal_circ, optimal_prob_s = results[minim_index]
if fourier_parametrise:
next_point = convert_from_fourier_point( optimal_qaoa_result.optimal_point, 2*p )
next_point = convert_to_fourier_point( interp_point(next_point), 2*p + 2 )
# next_point = np.zeros(shape = 2*p + 2)
# next_point[0:p] = optimal_qaoa_result.optimal_point[0:p]
# next_point[p+1:2*p+1] = optimal_qaoa_result.optimal_point[p:2*p]
else:
next_point = interp_point(optimal_qaoa_result.optimal_point)
if construct_circ:
print_to_file(optimal_circ.draw(fold=150))
minim_exp_val = exp_vals[minim_index]
approx_ratio = 1.0 - np.abs( (opt_value - minim_exp_val ) / opt_value )
print_to_file(" "+"Minimum: {}, prob_s: {}, approx_ratio {}".format(minim_exp_val, optimal_prob_s, approx_ratio))
approx_ratios.append(approx_ratio)
prob_s_s.append(optimal_prob_s)
print_to_file("-"*50)
print_to_file("QAOA terminated")
print_to_file("-"*50)
print_to_file("Approximation ratios per layer: {}".format(approx_ratios))
print_to_file("Prob_success per layer: {}".format(prob_s_s))
save_results = np.append(approx_ratios, prob_s_s)
if fourier_parametrise:
with open('results_{}cars{}routes/RI_F_{}.csv'.format(args["no_cars"], args["no_routes"], args["no_samples"]), 'w') as f:
np.savetxt(f, save_results, delimiter=',')
print_to_file("Results saved in results_{}cars{}routes/RI_F_{}.csv".format(args["no_cars"], args["no_routes"], args["no_samples"]))
else:
with open('results_{}cars{}routes/RI_NF_{}.csv'.format(args["no_cars"], args["no_routes"], args["no_samples"]), 'w') as f:
np.savetxt(f, save_results, delimiter=',')
print_to_file("Results saved in results_{}cars{}routes/RI_NF_{}.csv".format(args["no_cars"], args["no_routes"], args["no_samples"]))
finish = time()
print_to_file("Time Taken: {}".format(finish - start))