Using Ultra-Efficient Ising Machines for Complex Problem Solving
Research on “parametric frequency divider based Ising machines” conducted by Nicolas Casilli, PhD’26, electrical engineering, was accepted for publication in Physical Review Letters. Casilli conducted his research in Associate Professor Cristian Cassella’s lab and it is part of an NSF-funded grant on Massive Scale Computing and Optimization through On-chip ParameTric Ising MAchines (OPTIMA).
Abstract
We report on a new class of Ising machines (IMs) that rely on coupled parametric frequency dividers (PFDs) as macroscopic artificial spins. Unlike the IM counterparts based on subharmonic-injection locking (SHIL), PFD IMs do not require strong injected continuous-wave signals or applied DC voltages. Therefore, they show a significantly lower power consumption per spin compared to SHIL-based IMs, making it feasible to accurately solve large-scale combinatorial optimization problems (COPs) that are hard or even impossible to solve by using the current von Neumann computing architectures. %An analytical model describing the response of a coupled system of identical PFDs is presented and used to solve Max-Cut problems. Furthermore, using high quality () factor resonators in the PFD design makes PFD IMs able to exhibit a nanoWatt-level power-per-spin. Also, it remarkably allows a speed-up of the phase synchronization among the PFDs, resulting in shorter time-to-solution and lower energy-to-solution despite the resonators’ longer relaxation time. As a proof of concept, a 4-node PFD IM has been demonstrated. This IM correctly solves a set of Max-Cut problems while consuming just 600 nanoWatts per-spin. This power consumption is two orders of magnitude lower than the power-per-spin of state-of-the-art SHIL-based IMs operating at the same frequency.