# Multi‐State Probabilistic Computing Using Floating‐Body MOSFETs Based on the Potts Model for Solving Complex Combinatorial Optimization Problems

## TL;DR

This paper introduces a new computing system using special transistors to solve complex optimization problems more efficiently.

## Contribution

A novel multi-state probabilistic computing system using Potts model p-bits with FB-MOSFETs is introduced for complex COPs.

## Key findings

- The system efficiently samples a tunable Boltzmann distribution for COPs.
- It converges faster and is more energy-efficient than traditional methods.
- Experiments on spin glass and max-4-cut problems validate its effectiveness.

## Abstract

Probabilistic computing has gained attention for solving combinatorial optimization problems (COPs), mainly using the Ising model, which may not be suitable for complex COPs. Instead, this work proposes a multi‐state probabilistic computing system based on the Potts model using stochastic threshold switching floating‐body metal‐oxide‐semiconductor field‐effect transistors (FB‐MOSFETs) as the multi‐state probabilistic bits (p‐bits) to solve challenging COPs. The system employs drain voltage sharing and a one‐hot sampling method to achieve controllable probabilistic behavior and scalable annealing. Experimental validations on spin glass and max‐4‐cut problems demonstrate that the system efficiently samples a tunable Boltzmann distribution while converging faster than traditional methods. Comparative analyses further highlight superior energy efficiency and decreased time‐to‐solution, underscoring the potential of multi‐state probabilistic computing for large‐scale, complex COPs using only MOSFET devices.

This work introduces a multi‐state probabilistic computing system based on Potts‐model p‐bits using stochastic switching floating body metal oxide semiconductor field effect transistors (FB‐MOSFETs). By employing drain‐voltage sharing and one‐hot sampling, the system achieves controllable probabilistic behavior. Experiments on spin‐glass and max‐4‐cut problems demonstrate faster convergence and improved energy efficiency, highlighting its suitability for complex combinatorial optimization problems.