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Leveraging interactions for energy-efficient swarm-based Brownian computing

Alessandro Pignedoli, Atreya Majumdar*, and Karin Everschor-Sitte†

  • *Contact author: atreya.majumdar@uni-due.de
  • †Contact author: karin.everschor-sitte@uni-due.de
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Phys. Rev. Research 8, 033381 – Published 30 September, 2026

DOI: https://doi.org/10.1103/k8wj-xsl7

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 noninteracting 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.

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Phys. Rev. Research 8, 033381– Published 30 September, 2026
  • Received 20 March 2026
  • Accepted 6 August 2026
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