Leveraging Interactions for Efficient Swarm-Based Brownian Computing

TL;DR

This paper demonstrates that short-range interactions among Brownian quasiparticles can be harnessed for energy-efficient, scalable optimization, outperforming non-interacting searchers through emergent cooperative behavior.

Contribution

It introduces a physical model of interacting Brownian quasiparticles for optimization, showing their robustness and efficiency in complex, evolving landscapes.

Findings

Interacting swarms reliably find global optima.

Swarm performance surpasses non-interacting searchers.

The model is adaptable to dynamic environments.

Abstract

Drawing inspiration from swarm intelligence, we show that short-range attractive interactions between thermally driven Brownian quasiparticles enable energy-efficient optimization. As quasiparticles can be generated directly within a material, the swarm size can be adjusted with minimal energy overhead. Using an optimization task defined by a spatially varying temperature landscape, we quantitatively show that interacting swarms reliably identify global optima and significantly outperform non-interacting searchers within a well-defined regime of interaction strength and swarm size. This improvement arises from emergent cooperative behavior, where local interactions guide the swarm toward high-quality solutions without central coordination. To link our physical model to experimental realizations, we coarse-grain the quasiparticle dynamics onto a sensor lattice and generate trajectories emulating particle-tracking measurements. We further show that the interacting swarm adapts robustly to landscapes that evolve over time. These findings establish interacting Brownian quasiparticles as a physical platform for scalable and energy-efficient unconventional computing.