Superconductor Amoeba-Inspired Problem Solvers for Combinatorial Optimization

Adiabatic quantum-flux-parametron (AQFP) logic is an energy-efficient superconductor logic family; the energy dissipation of an AQFP gate can be arbitrarily reduced through adiabatic switching. In addition to high energy efficiency, AQFP logic has the advantage that it can easily introduce stochastic processes by exploiting naturally occurring thermal fluctuations. We propose using AQFP logic to implement an amoeba-inspired problem solver (APS), which is a stochastic local search method to explore solutions to combinatorial optimization problems such as the Boolean satisfiability problem (SAT). We designed a superconductor amoeba-inspired problem solver (SAPS) using AQFP logic, which finds solutions to a simple logical constraint satisfaction problem in the manner of APS, and fabricate it using a Nb integrated circuit fabrication process. Experimental results show that the probability distribution of the stochastic processes in AQFP logic can be controlled by the magnitude of bias current and that SAPS finds solutions using a small number of iterations when a moderate bias current is applied. The present results indicate the possibility of using AQFP logic to build hardware dedicated to the implementation of stochastic local search algorithms to solve combinatorial optimization problems such as SAT.