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Quantum annealers (QAs) are specialized quantum computers that minimizeobjective functions over discrete variables by physically exploiting quantumeffects. Current QA platforms allow for the optimization of quadraticobjectives defined over binary variables (qubits), also known as Isingproblems. In the last decade, QA systems as implemented by D-Wave have scaledwith Moore-like growth. Current architectures provide 2048 sparsely-connectedqubits, and continued exponential growth is anticipated, together withincreased connectivity. We explore the feasibility of such architectures forsolving SAT and MaxSAT problems as QA systems scale. We develop techniques foreffectively encoding SAT -and, with some limitations, MaxSAT- into Isingproblems compatible with sparse QA architectures. We provide the theoreticalfoundations for this mapping, and present encoding techniques that combineoffline Satisfiability and Optimization Modulo Theories with on-the-flyplacement and routing. Preliminary empirical tests on a current generation2048-qubit D-Wave system support the feasibility of the approach for certainSAT and MaxSAT problems.

Solving SAT and MaxSAT with a Quantum Annealer: Foundations, Encodings, and Preliminary Results