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. In this paper, 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 fabricated it using a Nb integrated circuit fabrication process. Experimental results showed that the probability distribution of the stochastic processes in AQFP logic can be