Max Cut

import networkx as nx
import numpy as np
from qat.opt import MaxCut
from qat.qpus import SimulatedAnnealing
from qat.simulated_annealing import integer_to_spins
from qat.core import Variable

# Specify the graph
graph = nx.full_rary_tree(2, 30)
max_cut_problem = MaxCut(graph)


# Extract parameters for SA
problem_parameters_dict = max_cut_problem.get_best_parameters()
n_steps = problem_parameters_dict["n_steps"]
temp_max = problem_parameters_dict["temp_max"]
temp_min = problem_parameters_dict["temp_min"]

# Create a temperature schedule and a QPU
tmax = 1.0
t = Variable("t", float)
temp_t = temp_min * (t / tmax) + temp_max * (1 - t / tmax)
sa_qpu = SimulatedAnnealing(temp_t=temp_t, n_steps=n_steps)

# Create a job and send it to the QPU
problem_job = max_cut_problem.to_job('sqa', tmax=tmax)
problem_result = sa_qpu.submit(problem_job)

# Extract and print solution configuration
state = problem_result.raw_data[0].state.int  # raw_data is a list of Samples - one per computation
solution_configuration = integer_to_spins(state, len(graph.nodes()))
print("Solution configuration: \n" + str(solution_configuration) + "\n")
indices_spin_1 = np.where(solution_configuration == 1)[0]
print("The nodes in the first subgraph:\n" + str(indices_spin_1) + "\n")
indices_spin_minus_1 = np.where(solution_configuration == -1)[0]
print("The nodes in the second subgraph:\n" + str(indices_spin_minus_1))