def test_individual_no_pairs(self):
edges_problem = [(0, 1), (1, 2), (2, 0), (3, 4), (4, 3)]
q = qubo.get_Q(edges_problem)
state_triangle = [(0, 1), (1, 2), (2, 0)]
energy_state_triangle = qubo.calculate_energy_for_state(q, state_triangle)
state_triangle_and_pair = [(0, 1), (1, 2), (2, 0), (3, 4), (4, 3)]
energy_state_triangle_and_pair = qubo.calculate_energy_for_state(q, state_triangle_and_pair)
assert energy_state_triangle_and_pair > energy_state_triangle
assert energy_state_triangle == -3
assert np.isclose(energy_state_triangle_and_pair, -5 + 2.01)
def solve(input_graph: list, params: dict) -> pd.DataFrame:
"""Find the frequency of valid solutions for a given input graph with
specified parameters for the solver algorithm.
When the a solution is present, also outputs the edges defining the
solution(s).
:param input_graph: graph defining the problem to be solved
:param params: parameters to be used by the quantum solver
:return: dataframe containing the frequency of solution, and the edges
defining the solution (when a solution is present)
"""
t0 = time.time()
Q = get_Q(input_graph)
t1 = time.time()
time_qubo = t1 - t0
print(f"Time to get Q: {time_qubo:.2f} s")
t2 = time.time()
response = _get_dwave_response(
Q,
params['num_reads'],
params['anneal_time'],
params['pause_duration'],
params['pause_start'],
params['seed_embedding'],
)
t3 = time.time()
time_dwave_response = t3 - t2
print(f"D-Wave time (including finding embedding): "
f"{time_dwave_response:.2f} s")
states_df = _extract_states_and_counts(response)
states_df['relative_frequency'] = states_df['absolute_frequency'] \
/ params['num_reads']
enriched_states_df, solution_frequency, edges_solution = \
get_valid_solutions(states_df, input_graph)
print(f'the frequency is {solution_frequency:.2%}')
if 0 < solution_frequency < 1:
runs_to_solution = np.log(1 - 0.99) / np.log(1 - solution_frequency)
elif solution_frequency == 1:
runs_to_solution = 0
else:
runs_to_solution = None
t4 = time.time()
time_overall_computation = t4 - t0
print(f"Time to solve overall: {time_overall_computation:.2f} s")
data = [[
params['tag'], params['n_cycles'], params['cycle_length'],
params['n_vertices'], params['p_noise'], params['n_edges_noise'],
params['seed_input_graph'], params['seed_embedding'],
params['num_reads'], params['anneal_time'], params['pause_duration'],
params['pause_start'], time_qubo, time_dwave_response,
time_overall_computation, solution_frequency, runs_to_solution,
input_graph, edges_solution,
enriched_states_df.to_json(orient='records'),
json.dumps(response.info['embedding_context'])
]]
return data
