def main():
nodes_array = TSP_utilities.create_nodes_array(4)
tsp_matrix = TSP_utilities.get_tsp_matrix(nodes_array)
starting_node = 0
print("Brute Force solution")
start_time = time.time()
bf_start_time = start_time
brute_force_solution = TSP_utilities.solve_tsp_brute_force_from_given_node(
nodes_array, starting_node)
end_time = time.time()
calculation_time = end_time - start_time
print("Calculation time:", calculation_time)
TSP_utilities.plot_solution('brute_force_' + str(start_time), nodes_array,
brute_force_solution)
print("QAOA solution - Forest")
start_time = time.time()
forest_solver = ForestTSPSolver(tsp_matrix, starting_node=starting_node)
forest_solution, forest_distribution = forest_solver.solve_tsp()
end_time = time.time()
calculation_time = end_time - start_time
print("Calculation time:", calculation_time)
costs = [(sol, TSP_utilities.calculate_cost(tsp_matrix,
sol), forest_distribution[sol])
for sol in forest_distribution]
print("Forest:")
for cost in costs:
print(cost)
TSP_utilities.plot_solution('forest_' + str(bf_start_time), nodes_array,
forest_solution)
def calculate_solution(self):
"""
Samples the QVM for the results of the algorithm
and returns a list containing the order of nodes.
"""
most_frequent_string, sampling_results = self.qaoa_inst.get_string(self.betas, self.gammas, samples=10000)
reduced_solution = TSP_utilities.binary_state_to_points_order(most_frequent_string)
full_solution = self.get_solution_for_full_array(reduced_solution)
self.solution = full_solution
all_solutions = sampling_results.keys()
distribution = {}
for sol in all_solutions:
reduced_sol = TSP_utilities.binary_state_to_points_order(sol)
full_sol = self.get_solution_for_full_array(reduced_sol)
distribution[tuple(full_sol)] = sampling_results[sol]
self.distribution = distribution
def decode_solution(self, response):
n = len(self.distance_matrix)
distribution = {}
min_energy = response.record[0].energy
for record in response.record:
sample = record[0]
solution_binary = [node for node in sample]
solution = TSP_utilities.binary_state_to_points_order(solution_binary)
distribution[tuple(solution)] = (record.energy, record.num_occurrences)
if record.energy <= min_energy:
self.solution = solution
self.distribution = distribution
def solveTSPinstance(instance):
nodes_array = TSP_utilities.readInstance(instance)
tsp_matrix = TSP_utilities.get_tsp_matrix(nodes_array)
sapi_token = None
dwave_url = 'https://cloud.dwavesys.com/sapi'
start_time = time.time()
bf_start_time = start_time
# print("Brute Force solution")
# brute_force_solution = TSP_utilities.solve_tsp_brute_force_from_given_node(nodes_array, starting_node)
# end_time = time.time()
# calculation_time = end_time - start_time
# print("Calculation time:", calculation_time)
# TSP_utilities.plot_solution('brute_force_' + str(start_time), nodes_array, brute_force_solution)
if sapi_token is None or dwave_url is None:
print(
"You cannot run code on DWave without specifying your sapi_token and url"
)
elif len(nodes_array) >= 40:
print("This problem size is to big to run on D-Wave.")
else:
print("DWave solution")
start_time = time.time()
dwave_solver = DWaveTSPSolver(tsp_matrix,
sapi_token=sapi_token,
url=dwave_url)
dwave_solution, dwave_distribution = dwave_solver.solve_tsp()
end_time = time.time()
calculation_time = end_time - start_time
print("Calculation time:", calculation_time)
costs = [(sol, TSP_utilities.calculate_cost(tsp_matrix, sol),
dwave_distribution[sol]) for sol in dwave_distribution]
solution_cost = TSP_utilities.calculate_cost(tsp_matrix,
dwave_solution)
print("DWave:", dwave_solution, solution_cost)
for cost in costs:
print(cost)
TSP_utilities.plot_solution('dwave_' + str(bf_start_time), nodes_array,
dwave_solution)
def main():
# seed = 5406
# nodes_array = TSP_utilities.create_nodes_array(4, seed=seed)
nodes_array = TSP_utilities.create_nodes_array(3)
print("Brute Force solution")
start_time = time.time()
brute_force_solution = TSP_utilities.solve_tsp_brute_force(nodes_array)
end_time = time.time()
calculation_time = end_time - start_time
print("Calculation time:", calculation_time)
TSP_utilities.plot_solution('brute_force', nodes_array,
brute_force_solution)
print("QAOA solution - Forest")
start_time = time.time()
forest_solver = ForestTSPSolver(nodes_array)
forest_solution = forest_solver.solve_tsp()
end_time = time.time()
calculation_time = end_time - start_time
print("Calculation time:", calculation_time)
TSP_utilities.plot_solution('forest_' + str(start_time), nodes_array,
forest_solution)
def main():
nodes_array = TSP_utilities.create_nodes_array(4)
tsp_matrix = TSP_utilities.get_tsp_matrix(nodes_array)
starting_node = 0
sapi_token = None
dwave_url = 'https://cloud.dwavesys.com/sapi'
print("Brute Force solution")
start_time = time.time()
bf_start_time = start_time
brute_force_solution = TSP_utilities.solve_tsp_brute_force_from_given_node(
nodes_array, starting_node)
end_time = time.time()
calculation_time = end_time - start_time
print("Calculation time:", calculation_time)
TSP_utilities.plot_solution('brute_force_' + str(start_time), nodes_array,
brute_force_solution)
if sapi_token is None or dwave_url is None:
print(
"You cannot run code on DWave without specifying your sapi_token and url"
)
else:
print("DWave solution")
start_time = time.time()
dwave_solver = DWaveTSPSolver(tsp_matrix,
sapi_token=sapi_token,
url=dwave_url)
dwave_solution, dwave_distribution = dwave_solver.solve_tsp()
end_time = time.time()
calculation_time = end_time - start_time
print("Calculation time:", calculation_time)
costs = [(sol, TSP_utilities.calculate_cost(tsp_matrix, sol),
dwave_distribution[sol]) for sol in dwave_distribution]
print("DWave:")
for cost in costs:
print(cost)
TSP_utilities.plot_solution('dwave_' + str(bf_start_time), nodes_array,
dwave_solution)
print("QAOA solution - Forest")
start_time = time.time()
forest_solver = ForestTSPSolver(tsp_matrix, starting_node=starting_node)
forest_solution, forest_distribution = forest_solver.solve_tsp()
end_time = time.time()
calculation_time = end_time - start_time
print("Calculation time:", calculation_time)
costs = [(sol, TSP_utilities.calculate_cost(tsp_matrix,
sol), forest_distribution[sol])
for sol in forest_distribution]
print("Forest:")
for cost in costs:
print(cost)
TSP_utilities.plot_solution('forest_' + str(bf_start_time), nodes_array,
forest_solution)