def main(args=None):
"""Brings all methods together to return a traffic routing instance.
Args:
raw_args (list, optional): list of strings to describe arguments. Defaults to None, but function fails if required arguments are not included
Returns:
list: In position 0, returns the graph of problem, In position 1, a Numpy Array with shape "no_cars" by "no_routes" given by argument for the generated routes for each car with the route descibed as a sequence of nodes of the graph.
"""
if args == None:
args = parse()
G = import_map("melbourne_2.pkl")
cpus = mp.cpu_count()
pool = mp.Pool(cpus)
while True:
cars_origin_destination = generate_start_finish_points(
G, args["no_cars"])
params = (
(G, start_destination, args["no_routes"])
for start_destination in list(cars_origin_destination.values()))
results = pool.starmap(generate_routes, params)
results = np.array(results, dtype=object)
if results.shape == (
args["no_cars"], args["no_routes"]
): #If results returns "no_cars" x "no_routes" shape, then it is valid
break
pool.close()
pool.join()
if args["visual"] == True:
visualise(G, results)
return G, results
def main(args=None):
#Generate and save valid qubos
if args == None:
args = parse()
print("Arguments: {}".format(args))
print("Now generating QUBOs with {} cars {} routes".format(
args["no_cars"], args["no_routes"]))
for qubo_no in tqdm(range(args["no_samples"])):
generate_valid_qubo(args, qubo_no)
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)
# 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()
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
else:
qubo, max_coeff, operator, offset, routes, classical_result = load_data
#Normalize qubo (Can recover original qubo via normalize_factor * qubo_objective )
qubo, normalize_factor = reduce_qubo(qubo)
#Initialize RQAOA object
rqaoa = RQAOA(qubo,
args["no_cars"],
args["no_routes"],
symmetrise=args["symmetrise"])
print("Args: {}".format(args))
print("First round of TQA-QAOA...")
p = args["p_max"]
qaoa_results = rqaoa.solve_qaoa(p, tqa=True)
rqaoa.perform_substitution_from_qaoa_results(qaoa_results,
biased=args["bias"])
print(
"Performed variable substition(s) and constructed new initial state and mixer."
)
num_vars = rqaoa.qubo.get_num_vars()
print("Remaining variables: {}".format(num_vars))
t = 1
#Recursively substitute variables to reduce qubit by one until 1 remaining
while num_vars > 1:
t += 1
qaoa_results = rqaoa.solve_qaoa(p, tqa=True)
print("Round {} of TQA-QAOA. Results below:".format(t))
rqaoa.perform_substitution_from_qaoa_results(qaoa_results,
biased=args["bias"])
print(
"Performed variable substition(s) and constructed new initial state and mixer."
)
num_vars = rqaoa.qubo.get_num_vars()
print("Remaining variables: {}".format(num_vars))
print("Final round of QAOA Done. Eigenstate below:")
p = 2
points = [[np.pi * (np.random.rand() - 0.5) for _ in range(2 * p)]
for _ in range(10)] + [[0 for _ in range(2 * p)]]
qaoa_results = rqaoa.solve_qaoa(p, points=points)
print("Probabilities: {}".format(rqaoa.prob_s))
print("Approx Qualities of (lowest_energy_state, most_probable_state): {}".
format(rqaoa.approx_s))
if args["symmetrise"]:
var_last = "X_anc" #Last variable should be ancilla if Hamiltonian was initially symmetrised
rqaoa.var_values[
var_last] = 0 #Now use the fact that ancilla should be in 0 state (so Z_anc = 1)
else:
max_state = sorted(qaoa_results.eigenstate,
key=lambda x: x[2],
reverse=True)[0]
var_last = rqaoa.qubo.variables[0].name
rqaoa.var_values[var_last] = int(max_state[0])
#Now read solution from correlations and final value
var_values = rqaoa.var_values
replacements = rqaoa.replacements
while True:
for var, replacement in replacements.items():
if replacement == None:
continue
elif replacement[
0] in var_values and var not in var_values and replacement[
1] != None:
var_values[var] = var_values[replacement[0]] if replacement[
1] == 1 else 1 - var_values[replacement[0]]
if len(var_values.keys()
) == args["no_cars"] * args["no_routes"] + 1 and args[
"symmetrise"]: #1 extra value if using ancilla
break
elif len(var_values.keys()) == args["no_cars"] * args["no_routes"]:
break
#Remove Ancilla qubit in final result if it is in the variable values dict
if "X_anc" in var_values:
var_values.pop("X_anc")
list_values = list(var_values.values())
cost = rqaoa.original_qubo.objective.evaluate(var_values)
print(rqaoa.result)
print(var_values)
print("{}, Cost: {}".format(list_values, cost))
#Save results
save_results = np.append(rqaoa.prob_s, rqaoa.approx_s)
if args["bias"]:
with open(
'results_{}cars{}routes_mps/Biased_RQAOA_{}_p={}.csv'.format(
args["no_cars"], args["no_routes"], args["no_samples"],
args["p_max"]), 'w') as f:
np.savetxt(f, save_results, delimiter=',')
print(
"Results saved in results_{}cars{}routes_mps/Biased_RQAOA_{}_p={}.csv"
.format(args["no_cars"], args["no_routes"], args["no_samples"],
args["p_max"]))
elif args["symmetrise"]:
with open(
'results_{}cars{}routes_mps/Symmetrised_RQAOA_{}_p={}.csv'.
format(args["no_cars"], args["no_routes"], args["no_samples"],
args["p_max"]), 'w') as f:
np.savetxt(f, save_results, delimiter=',')
print(
"Results saved in results_{}cars{}routes_mps/Symmetrised_RQAOA_{}_p={}.csv"
.format(args["no_cars"], args["no_routes"], args["no_samples"],
args["p_max"]))
else:
with open(
'results_{}cars{}routes_mps/Regular_RQAOA_{}_p={}.csv'.format(
args["no_cars"], args["no_routes"], args["no_samples"],
args["p_max"]), 'w') as f:
np.savetxt(f, save_results, delimiter=',')
print(
"Results saved in results_{}cars{}routes_mps/Regular_RQAOA_{}_p={}.csv"
.format(args["no_cars"], args["no_routes"], args["no_samples"],
args["p_max"]))
finish = time()
print("Time taken: {} s".format(finish - start))
def perform_substitution_from_qaoa_results(self, qaoa_results, update_benchmark_energy=True):
sorted_eigenstate_by_energy = sorted(qaoa_results.eigenstate, key = lambda x: x[1])
sorted_eigenstate_by_prob = sorted(qaoa_results.eigenstate, key = lambda x: x[2], reverse = True)
scale = self.random_energy - self.result.fval
approx_quality = (self.random_energy - sorted_eigenstate_by_energy[0][1])/ scale
eigenstate = dict( [ (x[0], x[2]) for x in qaoa_results.eigenstate ] )
self.approx_quality = approx_quality
self.eigenstate = eigenstate
print( "QAOA lowest energy solution: {}".format(sorted_eigenstate_by_energy[0]) )
print( "Approx_quality: {}".format(approx_quality)
print( "QAOA most probable solution: {}".format(sorted_eigenstate_by_prob[0]) )
print( "Approx_quality: {}".format((self.random_energy - sorted_eigenstate_by_prob[0][1])/ scale) )
correlations = self.get_correlations(qaoa_results.eigenstate)
i, j = self.find_strongest_correlation(correlations)
new_qubo = deepcopy(self.qubo)
x_i, x_j = new_qubo.variables[i].name, new_qubo.variables[j].name
print( "\nCorrelation: < {} {} > = {}".format(x_i, x_j, correlations[i, j]))
car_block = int(x_i[2])
#If same car_block and x_i = x_j, then both must be 0 since only one 1 in a car block
if x_i[2] == x_j[2] and correlations[i, j] > 0 and len(self.car_blocks[car_block]) > 2:
# set x_i = x_j = 0
new_qubo = new_qubo.substitute_variables({x_i: 0, x_j:0})
if new_qubo.status == QuadraticProgram.Status.INFEASIBLE:
raise QiskitOptimizationError('Infeasible due to variable substitution {} = {} = 0'.format(x_i, x_j))
self.var_values[x_i] = 0
self.var_values[x_j] = 0
self.car_blocks[car_block].remove(x_i)
self.car_blocks[car_block].remove(x_j)
print("Two variable substitutions were performed due to extra information from constraints.")
if len(self.car_blocks[car_block]) == 1: #If only one remaining variable
x_r = self.car_blocks[car_block][0] #remaining variable
#Check if all other variables are 0 (then their sum should be 0) -> so x_r must be 1
check = sum( [self.var_values.get("X_{}_{}".format(car_block, route_no), 0) for route_no in range(self.no_routes)] )
if check == 0:
new_qubo = new_qubo.substitute_variables({x_r: 1})
if new_qubo.status == QuadraticProgram.Status.INFEASIBLE:
raise QiskitOptimizationError('Infeasible due to variable substitution {} = 1'.format(x_r))
self.car_blocks[car_block].remove(x_r)
print("{} = 1 can also be determined from all other variables being 0 for car_{}".format(x_r, car_block))
elif x_i[2] != x_j[2] and correlations[i, j] > 0:
# set x_i = x_j
new_qubo = new_qubo.substitute_variables(variables={i: (j, 1)})
if new_qubo.status == QuadraticProgram.Status.INFEASIBLE:
raise QiskitOptimizationError('Infeasible due to variable substitution {} = {}'.format(x_i, x_j))
self.replacements[x_i] = (x_j, 1)
self.car_blocks[car_block].remove(x_i)
else:
# set x_i = 1 - x_j, this is done in two steps:
# 1. set x_i = 1 + x_i
# 2. set x_i = -x_j
# 1a. get additional offset from the 1 on the RHS of (xi -> 1+xi)
constant = new_qubo.objective.constant
constant += new_qubo.objective.linear[i]
constant += new_qubo.objective.quadratic[i, i]
new_qubo.objective.constant = constant
#1b get additional linear part from quadratic terms becoming linear due to the same 1 in the 1+xi as above
for k in range(new_qubo.get_num_vars()):
coeff = new_qubo.objective.linear[k]
if k == i:
coeff += 2*new_qubo.objective.quadratic[i, k]
else:
coeff += new_qubo.objective.quadratic[i, k]
# set new coefficient if not too small
if np.abs(coeff) > 1e-10:
new_qubo.objective.linear[k] = coeff
else:
new_qubo.objective.linear[k] = 0
#2 set xi = -xj
new_qubo = new_qubo.substitute_variables(variables={i: (j, -1)})
if new_qubo.status == QuadraticProgram.Status.INFEASIBLE:
raise QiskitOptimizationError('Infeasible due to variable substitution {} = -{}'.format(x_i, x_j))
self.replacements[x_i] = (x_j, -1)
self.car_blocks[car_block].remove(x_i)
self.qubo = new_qubo
op, offset = new_qubo.to_ising()
self.operator = op
self.offset = offset
self.construct_initial_state()
self.construct_mixer()
if update_benchmark_energy:
temp = self.get_benchmark_energy()
def get_correlations(self, state) -> np.ndarray:
"""
Get <Zi x Zj> correlation matrix from the eigenstate(state: Dict).
Returns:
A correlation matrix.
"""
states = state
# print(states)
x, _, prob = states[0]
n = len(x)
correlations = np.zeros((n, n))
for x, cost, prob in states:
if cost < self.benchmark_energy:
scaled_approx_quality = (self.benchmark_energy - cost)
for i in range(n):
for j in range(i):
if x[i] == x[j]:
correlations[i, j] += scaled_approx_quality * prob
else:
correlations[i, j] -= scaled_approx_quality * prob
return correlations
def find_strongest_correlation(self, correlations):
# get absolute values and set diagonal to -1 to make sure maximum is always on off-diagonal
abs_correlations = np.abs(correlations)
diagonal = np.diag( np.ones(len(correlations)) )
abs_correlations = abs_correlations - diagonal
# get index of maximum (by construction on off-diagonal)
m_max = np.argmax(abs_correlations.flatten())
# translate back to indices
i = int(m_max // len(correlations))
j = int(m_max - i*len(correlations))
return (i, j)
def main(args=None):
start = time()
if args == None:
args = parse()
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
else:
qubo, max_coeff, operator, offset, routes, classical_result = load_data
rqaoa = RQAOA(qubo, args["no_cars"], args["no_routes"])
rqaoa.solve_classically()
print(rqaoa.result)
print("First round of TQA-QAOA. Results below:")
qaoa_results = rqaoa.solve_tqa_qaoa(2)
rqaoa.perform_substitution_from_qaoa_results(qaoa_results)
print("Performed variable substition(s) and constructed new initial state and mixer.")
num_vars = rqaoa.qubo.get_num_vars()
print("Remaining variables: {}".format(num_vars))
t = 1
while num_vars > 1:
print("-"*50)
t+=1
qaoa_results = rqaoa.solve_tqa_qaoa(2)
print( "Round {} of TQA-QAOA. Results below:".format(t) )
rqaoa.perform_substitution_from_qaoa_results(qaoa_results)
print("Performed variable substition(s) and constructed new initial state and mixer.")
num_vars = rqaoa.qubo.get_num_vars()
print("Remaining variables: {}".format(num_vars))
print("-"*50)
p=2
qaoa_results = rqaoa.solve_qaoa( p, point = [0]* (2*p) )
print( "Final round of QAOA Done. Eigenstate below:" )
pprint(qaoa_results.eigenstate)
print( rqaoa.prob_s )
print( rqaoa.approx_s )
max_state = sorted(qaoa_results.eigenstate, key = lambda x: x[2], reverse=True)[0]
var_last = rqaoa.qubo.variables[0].name
rqaoa.var_values[var_last] = int(max_state[0])
var_values = rqaoa.var_values
replacements = rqaoa.replacements
while True:
for var, replacement in replacements.items():
if replacement == None:
continue
elif replacement[0] in var_values and var not in var_values and replacement[1] != None:
var_values[var] = var_values[ replacement[0] ] if replacement[1] == 1 else 1 - var_values[ replacement[0] ]
if len(var_values.keys()) == args["no_cars"]*args["no_routes"]:
break
print(rqaoa.result)
print(var_values)
list_values = list(var_values.values())
cost = rqaoa.original_qubo.objective.evaluate(var_values)
print("{}, Cost: {}".format(list_values, cost))
if __name__ == '__main__':
main()
finish = time()
print("Time taken: {} s".format(finish - start))
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)
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))
def main(args=None):
start = time()
if args == None:
args = parse()
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
fourier_parametrise = args["fourier"]
#Normalize qubo (Can recover original qubo via normalize_factor * qubo_objective )
qubo, normalize_factor = reduce_qubo(qubo)
if classical_result:
classical_result._fval /= normalize_factor #Also normalize classical result
#Noise
if args["noisy"]:
#Use symmetrised Hamiltonian and base QAOA
args["symmetrise"] = True
args["customise"] = False
args["bias"] = False
args["simulator"] = "aer_simulator_density_matrix"
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 RQAOA object and make sure there is a classical solution
rqaoa = RQAOA(qubo,
args["no_cars"],
args["no_routes"],
symmetrise = args["symmetrise"],
customise = args["customise"],
classical_result = classical_result,
simulator = args["simulator"],
noise_model = noise_model,
opt_str = args["optimizer"]
)
print("Args: {}\n".format(args))
iterate_time = time()
print("First round of TQA-QAOA...")
p = args["p_max"]
qaoa_results = rqaoa.solve_qaoa(p, tqa=True, fourier_parametrise = fourier_parametrise) if p>1 else rqaoa.solve_qaoa(p, fourier_parametrise=fourier_parametrise)
rqaoa.perform_substitution_from_qaoa_results(qaoa_results, biased = args["bias"])
print("Performed variable substition(s).")
num_vars = rqaoa.qubo.get_num_vars()
print("Remaining variables: {}".format(num_vars))
iterate_time_2 = time()
print("Time taken (for this iteration): {}s".format(iterate_time_2 - iterate_time))
t = 1
#Recursively substitute variables to reduce qubit by one until 1 remaining
while num_vars > 1:
iterate_time = time()
t+=1
print( "\nRound {} of TQA-QAOA. Results below:".format(t) )
qaoa_results = rqaoa.solve_qaoa(p, tqa=True, fourier_parametrise=fourier_parametrise) if p>1 else rqaoa.solve_qaoa(p, fourier_parametrise=fourier_parametrise)
rqaoa.perform_substitution_from_qaoa_results(qaoa_results, biased = args["bias"])
print("Performed variable substition(s).")
num_vars = rqaoa.qubo.get_num_vars()
print("Remaining variables: {}".format(num_vars))
iterate_time_2 = time()
print("Time taken (for this iteration): {}s".format(iterate_time_2 - iterate_time))
print( "\nFinal round of QAOA. Eigenstate below:" )
p=2
points = [ [ np.pi * (np.random.rand() - 0.5) for _ in range(2*p) ] for _ in range(10) ] + [ [ 0 for _ in range(2*p) ] ]
qaoa_results = rqaoa.solve_qaoa( p, points = points )
print( "\nProbabilities: {}".format(rqaoa.prob_s) )
print( "Approx Qualities of (lowest_energy_state, most_probable_state): {}\n".format(rqaoa.approx_s) )
if args["symmetrise"]:
var_last = "X_anc" #Last variable should be ancilla if Hamiltonian was initially symmetrised
rqaoa.var_values[var_last] = 0 #Now use the fact that ancilla should be in 0 state (so Z_anc = 1)
else:
max_state = sorted(qaoa_results.eigenstate, key = lambda x: x[2], reverse=True)[0]
var_last = rqaoa.qubo.variables[0].name
rqaoa.var_values[var_last] = int(max_state[0])
#Now read solution from correlations and final value
var_values = rqaoa.var_values
replacements = rqaoa.replacements
while True:
for var, replacement in replacements.items():
if replacement == None:
continue
elif replacement[0] in var_values and var not in var_values and replacement[1] != None:
var_values[var] = var_values[ replacement[0] ] if replacement[1] == 1 else 1 - var_values[ replacement[0] ]
if len(var_values.keys()) == args["no_cars"]*args["no_routes"]+1 and args["symmetrise"]: #1 extra value if using ancilla
break
elif len(var_values.keys()) == args["no_cars"]*args["no_routes"]:
break
#Remove Ancilla qubit in final result if it is in the variable values dict
if "X_anc" in var_values:
var_values.pop("X_anc")
list_values = list(var_values.values())
cost = rqaoa.original_qubo.objective.evaluate(var_values)
print(rqaoa.classical_result)
print("\nRQAOA Solution: {}, Cost: {}".format(list_values, cost))
#End of algorithm
finish = time()
print("\nTime taken: {} s".format(finish - start))
#Naming of file to save results to
if args["customise"] and not args["noisy"]: #Using custom QAOA
if args["bias"]:
filedir = 'results_{}cars{}routes_mps/Biased_RQAOA_{}_Cust_p={}.csv'.format(args["no_cars"], args["no_routes"], args["no_samples"], args["p_max"])
elif args["symmetrise"]:
filedir = 'results_{}cars{}routes_mps/Symmetrised_RQAOA_{}_Cust_p={}.csv'.format(args["no_cars"], args["no_routes"], args["no_samples"], args["p_max"])
else:
filedir = 'results_{}cars{}routes_mps/Regular_RQAOA_{}_Cust_p={}.csv'.format(args["no_cars"], args["no_routes"], args["no_samples"], args["p_max"])
elif not args["customise"] and not args["noisy"]:
if args["bias"]: #Using baseline QAOA
filedir = 'results_{}cars{}routes_mps/Biased_RQAOA_{}_Base_p={}.csv'.format(args["no_cars"], args["no_routes"], args["no_samples"], args["p_max"])
elif args["symmetrise"]:
filedir = 'results_{}cars{}routes_mps/Symmetrised_RQAOA_{}_Base_p={}.csv'.format(args["no_cars"], args["no_routes"], args["no_samples"], args["p_max"])
else:
filedir = 'results_{}cars{}routes_mps/Regular_RQAOA_{}_Base_p={}.csv'.format(args["no_cars"], args["no_routes"], args["no_samples"], args["p_max"])
elif args["noisy"]:
filedir = 'results_{}cars{}routes/Noisy_S_RQAOA_{}_Base_p={}_Error={}.csv'.format(args["no_cars"], args["no_routes"], args["no_samples"], args["p_max"], args["multiplier"])
#Save results to file
save_results = np.append( rqaoa.prob_s, rqaoa.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)
print("_"*len(result_saved_string))
print("")
def main(args=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)
classical_result = solve_classically(qubo)
print(classical_result)
x_arr = classical_result.x
optimal_value = qubo.objective.evaluate(x_arr)
# print(optimal_value)
x_str = arr_to_str(x_arr)
sort_values = get_costs(qubo)
# print("_"*50)
# up_to = 27
# print("{} lowest states:".format(up_to))
# avg = 0
# for i in range(up_to):
# print(sort_values[i])
# avg += sort_values[i][1]
# print("_"*50)
# print("Avg: {}".format(avg/up_to))
#Remake QUBO to introduce only ZiZj terms
op, offset = qubo.to_ising()
new_operator = []
for i, op_1 in enumerate(op):
coeff, op_1 = op_1.to_pauli_op().coeff, op_1.to_pauli_op().primitive
op_1_str = op_1.to_label()
Z_counts = op_1_str.count('Z')
if Z_counts == 1:
op_1_str = 'Z' + op_1_str
else:
op_1_str = 'I' + op_1_str
pauli = PauliOp( primitive = Pauli(op_1_str), coeff = coeff )
new_operator.append(pauli)
qubo_2 = QuadraticProgram()
operator = sum(new_operator)
qubo_2.from_ising(operator, offset, linear=True)
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)
p = 1
delta_points = []
deltas = np.arange(0.75, 0.76, 0.05)
print("delta_t's: ", deltas)
original_point = np.append([], [[(i+1)/p, 1-(i+1)/p] for i in range(p)])
for delta in deltas:
delta_points.append(delta*original_point)
# point = [ 0.60081404, 0.11785113, 0.02330747, 1.10006101, 0.46256391,
# -0.96823671]
draw_circuit = True
initial_state = construct_initial_state_2(args["no_routes"], args["no_cars"])
mixer = n_qbit_mixer(initial_state)
quantum_instance = QuantumInstance(backend)
results = []
exp_vals = []
for point in delta_points:
point = QAOAEx.convert_to_fourier_point(point, 2*p)
qaoa_results, _ = Fourier_QAOA(operator,
quantum_instance,
optimizer,
reps = p,
initial_fourier_point=point,
initial_state = initial_state,
mixer = mixer,
construct_circ=draw_circuit,
fourier_parametrise = True
)
# print(optimal_circ.draw())
results.append(qaoa_results)
exp_val = qaoa_results.eigenvalue + offset
print("ExpVal {}".format(exp_val))
exp_vals.append(exp_val)
minim_index = np.argmin(exp_vals)
qaoa_results = results[minim_index]
sort_states = sorted(qaoa_results.eigenstate.items(), key=lambda x: x[1], reverse=True)
correlations = get_correlations(sort_states)
# print(correlations)
i,j = find_strongest_correlation(correlations)
if correlations[i,j] > 0:
condition = "Z_{i} = Z_{j}".format(i=i,j=j)
else:
condition = "Z_{i} = - Z_{j}".format(i=i,j=j)
print("Max: <Z_{i}Z_{j}>={maxim}, condition: ".format(i=i, j=j, maxim = correlations[i,j])+condition)
ground_energy = sort_values[0][1]
x_s = [x_str]
for i in range(0,10):
if sort_values[i][0] == x_str or np.round(sort_values[i][1], 4) != np.round(ground_energy, 4):
continue
else:
print("Other ground state(s) found: '{}'".format(sort_values[i][0]))
x_s.append(sort_values[i][0])
x_s_2 = []
print("All the possible solutions were originally: ")
print("[Z0 Z1 Z2 Z3 Z4 Z5 Z6 Z7 Z8 Z9]")
for string in x_s:
x = '0' + string
x_arr = np.array(str_to_arr(x))
# print("f({x})={obj}".format(x=x, obj = qubo_2.objective.evaluate(x_arr)))
x_s_2.append(x_arr)
formatted_arr = ["{} ".format(item) for item in x_arr]
formatted_arr = ' '.join(formatted_arr)
print("[{}]".format(formatted_arr))
finish = time()
print("Time taken {time}s".format(time=finish-start))