Introduction¶
Divideandconquer and dynamic programming algorithms have a rich history in computer science for problems with large numbers of variables. Many hard problems that can benefit from quantum computers are to large to map directly to a QPU. To solve a problem with more variables than the available number of qubits, we break the problem into subproblems, solve the subproblems, and then reconstruct an answer to the original problem from the subproblem solutions.
qbsolv is one such decomposing solver. It provides two interfaces:
Command Line Interface (CLI)
The tabu algorithm is executed on the problem which is divided into subproblems of several dozen variables each.

The Python interface provides a
QBSolv
class wrapper for the qbsolv C code. A dimod sampler can be substituted for the default tabu algorithm.
For a description of the algorithm and implementation, see
Partitioning Optimization Problems for Hybrid Classical/Quantum Execution
.
For a description of the tabu search algorithm, see Tabu search.
Example¶
This example sends 30variable subproblems of a 500variable QUBO to the dwaveneal sampler to be incorporated into the tabu results run in the main loop of qbsolv.
>>> from dwave_qbsolv import QBSolv
>>> import neal
>>> import itertools
>>> import random
...
>>> qubo_size = 500
>>> subqubo_size = 30
>>> Q = {t: random.uniform(1, 1) for t in itertools.product(range(qubo_size), repeat=2)}
>>> sampler = neal.SimulatedAnnealingSampler()
>>> response = QBSolv().sample_qubo(Q, solver=sampler, solver_limit=subqubo_size)
>>> print("energies=" + str(list(response.data_vectors['energy']))) # doctest: +SKIP
energies=[2800.794817495185]