Intrinsic optimization using stochastic nanomagnets

TL;DR

This paper proposes a hardware system using stochastic nanomagnets that naturally encode solutions to NP-hard problems through their collective states, leveraging physics described by the Ising model for efficient optimization.

Contribution

It introduces a novel nanomagnet-based hardware platform that intrinsically performs optimization by physics-guided state evolution, demonstrated through simulations of NP-complete problems.

Findings

Simulated solutions for NP-hard problems like TSP.

Nanomagnet interactions can be reconfigured for specific problems.

System operates at room temperature with GHz speeds.

Abstract

This paper draws attention to a hardware system which can be engineered so that its intrinsic physics is described by the generalized Ising model and can encode the solution to many important NP-hard problems as its ground state. The basic constituents are stochastic nanomagnets which switch randomly between the $\pm 1$ Ising states and can be monitored continuously with standard electronics. Their mutual interactions can be short or long range, and their strengths can be reconfigured as needed to solve specific problems and to anneal the system at room temperature. The natural laws of statistical mechanics guide the network of stochastic nanomagnets at GHz speeds through the collective states with an emphasis on the low energy states that represent optimal solutions. As proof-of-concept, we present simulation results for standard NP-complete examples including a 16-city traveling salesman problem using experimentally benchmarked models for spin-transfer torque driven stochastic nanomagnets.