def solve_qiskit_qaoa(self, p, **kwargs):
print("USING QISKIT CODE")
point = kwargs.get("point", None)
tqa = kwargs.get('tqa', False)
points = kwargs.get("points", None)
construct_circ = kwargs.get("construct_circ", False)
fourier_parametrise = kwargs.get("fourier_parametrise", False)
if tqa:
deltas = np.arange(0.25, 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]
if fourier_parametrise:
points = [
convert_to_fourier_point(point, len(point))
for point in points
]
qaoa_results, circ = QiskitQAOA(
self.operator,
self.quantum_instance,
self.optimizer,
reps=p,
initial_state=self.initial_state,
mixer=self.mixer,
list_points=points,
construct_circ=construct_circ,
fourier_parametrise=fourier_parametrise)
elif points is not None:
if point is not None:
points.append(point)
if fourier_parametrise:
points = [
convert_to_fourier_point(point, len(point))
for point in points
]
qaoa_results, circ = QiskitQAOA(
self.operator,
self.quantum_instance,
self.optimizer,
reps=p,
initial_state=self.initial_state,
mixer=self.mixer,
list_points=points,
construct_circ=construct_circ,
fourier_parametrise=fourier_parametrise)
elif point is not None:
if fourier_parametrise:
point = convert_to_fourier_point(point, 2 * p)
qaoa_results, circ = QiskitQAOA(
self.operator,
self.quantum_instance,
self.optimizer,
reps=p,
initial_state=self.initial_state,
initial_point=point,
mixer=self.mixer,
construct_circ=construct_circ,
fourier_parametrise=fourier_parametrise)
else:
points = [[0] * (2 * p)] + [[
1.98 * np.pi * (np.random.rand() - 0.5) for _ in range(2 * p)
] for _ in range(10)]
qaoa_results, circ = QiskitQAOA(
self.operator,
self.quantum_instance,
self.optimizer,
reps=p,
initial_state=self.initial_state,
list_points=points,
mixer=self.mixer,
construct_circ=construct_circ,
fourier_parametrise=fourier_parametrise)
if circ:
print(circ.draw(fold=200))
optimal_point = qaoa_results.optimal_point
self.optimal_point = optimal_point
self.qaoa_result = qaoa_results
eigenstate = qaoa_results.eigenstate
if self.quantum_instance.is_statevector:
from qiskit.quantum_info import Statevector
eigenstate = Statevector(eigenstate)
eigenstate = eigenstate.probabilities_dict()
else:
eigenstate = dict([(u, v**2) for u, v in eigenstate.items()
]) #Change to probabilities
num_qubits = len(list(eigenstate.items())[0][0])
solutions = []
eigenvalue = 0
for bitstr, sampling_probability in eigenstate.items():
bitstr = bitstr[::-1]
value = self.qubo.objective.evaluate([int(bit) for bit in bitstr])
eigenvalue += value * sampling_probability
solutions += [(bitstr, value, sampling_probability)]
qaoa_results.eigenstate = solutions
qaoa_results.eigenvalue = eigenvalue
#Sort states by decreasing probability
sorted_eigenstate_by_prob = sorted(qaoa_results.eigenstate,
key=lambda x: x[2],
reverse=True)
#print sorted state in a table
self.print_state(sorted_eigenstate_by_prob)
#Other print stuff
print("Eigenvalue: {}".format(eigenvalue))
print("Optimal point: {}".format(optimal_point))
print("Optimizer Evals: {}".format(qaoa_results.optimizer_evals))
scale = self.random_energy - self.opt_value
approx_quality_2 = np.round(
(self.random_energy - sorted_eigenstate_by_prob[0][1]) / scale, 3)
energy_prob = {}
for x in qaoa_results.eigenstate:
energy_prob[np.round(
x[1], 6)] = energy_prob.get(np.round(x[1], 6), 0) + x[2]
prob_s = np.round(energy_prob.get(np.round(self.opt_value, 6), 0), 6)
self.prob_s.append(prob_s)
self.eval_s.append(eigenvalue)
self.approx_s.append(approx_quality_2)
print("\nQAOA most probable solution: {}".format(
sorted_eigenstate_by_prob[0]))
print("Approx_quality: {}".format(approx_quality_2))
def solve_qaoa(self, p, **kwargs):
print("USING CUSTOM CODE")
point = kwargs.get(
"point", None
) #Make sure point here is already in FOURIER space of length 2(p-1)
fourier_parametrise = kwargs.get("fourier_parametrise", False)
tqa = kwargs.get('tqa', False)
points = kwargs.get("points", None)
construct_circ = kwargs.get("construct_circ", False)
if tqa:
deltas = np.arange(0.25, 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]
if fourier_parametrise:
points = [
convert_to_fourier_point(point, len(point))
for point in points
]
qaoa_results, circ = CustomQAOA(
self.operator,
self.quantum_instance,
self.optimizer,
reps=p,
initial_state=self.initial_state,
mixer=self.mixer,
fourier_parametrise=fourier_parametrise,
list_points=points,
qubo=self.qubo,
construct_circ=construct_circ)
elif points is not None:
if fourier_parametrise:
points = [
convert_to_fourier_point(point, len(point))
for point in points
]
if point is not None:
points.append(convert_to_fourier_point(point, len(point)))
else:
points.append(point)
qaoa_results, circ = CustomQAOA(
self.operator,
self.quantum_instance,
self.optimizer,
reps=p,
initial_state=self.initial_state,
mixer=self.mixer,
fourier_parametrise=fourier_parametrise,
list_points=points,
qubo=self.qubo,
construct_circ=construct_circ)
elif point is not None:
if fourier_parametrise:
initial_point = convert_to_fourier_point(point, len(point))
qaoa_results, circ = CustomQAOA(
self.operator,
self.quantum_instance,
self.optimizer,
reps=p,
initial_state=self.initial_state,
initial_point=point,
mixer=self.mixer,
fourier_parametrise=fourier_parametrise,
qubo=self.qubo,
construct_circ=construct_circ)
else:
points = [[0] * (2 * p)] + [[
1.98 * np.pi * (np.random.rand() - 0.5) for _ in range(2 * p)
] for _ in range(10)]
qaoa_results, circ = CustomQAOA(
self.operator,
self.quantum_instance,
self.optimizer,
reps=p,
initial_state=self.initial_state,
list_points=points,
mixer=self.mixer,
fourier_parametrise=fourier_parametrise,
qubo=self.qubo,
construct_circ=construct_circ)
if circ:
print(circ.draw(fold=200))
optimal_point = qaoa_results.optimal_point
eigenvalue = sum([x[1] * x[2] for x in qaoa_results.eigenstate])
qaoa_results.eigenvalue = eigenvalue
self.optimal_point = optimal_point
self.qaoa_result = qaoa_results
#Sort states by decreasing probability
sorted_eigenstate_by_prob = sorted(qaoa_results.eigenstate,
key=lambda x: x[2],
reverse=True)
#print sorted state in a table
self.print_state(sorted_eigenstate_by_prob)
#Other print stuff
print("Eigenvalue: {}".format(eigenvalue))
print("Optimal point: {}".format(optimal_point))
print("Optimizer Evals: {}".format(qaoa_results.optimizer_evals))
scale = self.random_energy - self.opt_value
approx_quality_2 = np.round(
(self.random_energy - sorted_eigenstate_by_prob[0][1]) / scale, 3)
energy_prob = {}
for x in qaoa_results.eigenstate:
energy_prob[np.round(
x[1], 6)] = energy_prob.get(np.round(x[1], 6), 0) + x[2]
prob_s = np.round(energy_prob.get(np.round(self.opt_value, 6), 0), 6)
self.prob_s.append(prob_s)
self.eval_s.append(eigenvalue)
self.approx_s.append(approx_quality_2)
print("\nQAOA most probable solution: {}".format(
sorted_eigenstate_by_prob[0]))
print("Approx_quality: {}".format(approx_quality_2))
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):
start = time()
if args == None:
args = parse()
#Load QUBO and reduce, then check existing classical solution
with open(
'qubos_{}_car_{}_routes/qubo_{}.pkl'.format(
args["no_cars"], args["no_routes"], args["no_samples"]),
'rb') as f:
load_data = pkl.load(f)
if len(load_data) == 5:
qubo, max_coeff, operator, offset, routes = load_data
classical_result = None
else:
qubo, max_coeff, operator, offset, routes, classical_result = load_data
qubo, normalize_factor = reduce_qubo(qubo)
if classical_result:
classical_result._fval /= normalize_factor #Also normalize classical result
print("NO CARS: {}, NO ROUTES: {}, SAMPLE: {}\n".format(
args["no_cars"], args["no_routes"], args["no_samples"]))
#Noise
if args["noisy"]:
multiplier = args["multiplier"]
print(
"Simulating with noise...Error mulitplier: {}".format(multiplier))
with open('average_gate_errors.json', 'r') as f:
noise_rates = json.load(f)
noise_model = build_noise_model(noise_rates, multiplier)
else:
print("Simulating without noise...")
noise_model = None
#Initialize QAOA object
qaoa = QAOA_Base(qubo=qubo,
no_cars=args["no_cars"],
no_routes=args["no_routes"],
symmetrise=args["symmetrise"],
customise=args["customise"],
classical_result=classical_result,
simulator=args["simulator"],
noise_model=noise_model,
opt_str=args["optimizer"])
p_max = args["p_max"]
fourier_parametrise = args["fourier"]
print("QAOA METHODS")
print("FOURIER: {}\nINTERP: {}".format(args["fourier"], args["interp"]))
print("CUSTOM: {}\nSYMMETR: {}".format(args["customise"],
args["symmetrise"]))
for p in range(1, p_max + 1):
p_start = time()
print("\nQAOA p={}. Results below:".format(p))
qaoa.optimizer.set_options(maxeval=100 * (2**p))
if p == 1:
qaoa.solve_qiskit_qaoa(p, fourier_parametrise=fourier_parametrise)
else:
if args["interp"]:
next_point = interp_point(qaoa.optimal_point)
else:
point = qaoa.optimal_point if not args[
"fourier"] else convert_to_fourier_point(
qaoa.optimal_point, len(qaoa.optimal_point))
next_point = np.zeros(2 * p)
next_point[0:p - 1] = point[0:p - 1]
next_point[p:2 * p - 1] = point[p - 1:2 * p - 2]
if args["fourier"]:
next_point = convert_from_fourier_point(next_point, 2 * p)
qaoa.solve_qiskit_qaoa(p,
point=next_point,
fourier_parametrise=fourier_parametrise)
p_end = time()
print("Time taken (for this iteration): {}s".format(p_end - p_start))
prob_s = [np.round(prob, 3) for prob in qaoa.prob_s]
eval_s = [np.round(e_val, 3) for e_val in qaoa.eval_s]
approx_s = [np.round(approx, 3) for approx in qaoa.approx_s]
print("\nProbabilities: {}".format(prob_s))
print("Eigenvalue at each p: {}".format(eval_s))
print("Approx Qualities of most_probable_state: {}\n".format(approx_s))
filedir = 'results_{}cars{}routes/'.format(args["no_cars"],
args["no_routes"])
p_max = str(args["p_max"]).zfill(2)
err_multipler = '{:.2f}'.format(
args["multiplier"]) if args["noisy"] else '{:.2f}'.format(0.0)
sample = str(args["no_samples"]).zfill(3)
if args["noisy"]:
filedir += "Noisy_QAOA_p={}_s={}_err={}".format(
p_max, sample, err_multipler)
else:
filedir += "Ideal_QAOA_p={}_s={}_err={}".format(
p_max, sample, err_multipler)
if args["symmetrise"]:
filedir += "_Symm"
else:
filedir += "_Base"
if args["customise"]:
filedir += "_Cust"
else:
filedir += "_Base"
if args["fourier"]:
filedir += "_FOUR"
else:
filedir += "_NONE"
if args["interp"]:
filedir += "_INTP"
else:
filedir += "_NONE"
filedir += "_{}".format(str(args["optimizer"]))
print(filedir)
#Save results to file
save_results = np.append(qaoa.prob_s, qaoa.eval_s)
save_results = np.append(save_results, qaoa.approx_s)
with open(filedir, 'w') as f:
np.savetxt(f, save_results, delimiter=',')
result_saved_string = "Results saved in {}".format(filedir)
print(result_saved_string)
finish = time()
print("\nTime taken: {} s".format(finish - start))
print("_" * len(result_saved_string))
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))