A decomposing solver that finds a minimum value of a large quadratic unconstrained binary optimization (QUBO) problem by splitting it into pieces. The pieces are solved using a classical solver running the tabu algorithm. qbsolv also enables configuring a D-Wave system as the solver.


Access to a D-Wave system must be arranged separately.


from dwave_qbsolv import QBSolv
Q = {(0, 0): 1, (1, 1): 1, (0, 1): 1}
response = QBSolv().sample_qubo(Q)
print("samples=" + str(list(response.samples())))
print("energies=" + str(list(response.data_vectors['energy'])))