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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.22874 |
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| _version_ | 1866912862706860032 |
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| author | Pignedoli, Alessandro Majumdar, Atreya Everschor-Sitte, Karin |
| author_facet | Pignedoli, Alessandro Majumdar, Atreya Everschor-Sitte, Karin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_22874 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Leveraging Interactions for Efficient Swarm-Based Brownian Computing Pignedoli, Alessandro Majumdar, Atreya Everschor-Sitte, Karin Statistical Mechanics Disordered Systems and Neural Networks Adaptation and Self-Organizing Systems Computational Physics Data Analysis, Statistics and Probability 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. |
| title | Leveraging Interactions for Efficient Swarm-Based Brownian Computing |
| topic | Statistical Mechanics Disordered Systems and Neural Networks Adaptation and Self-Organizing Systems Computational Physics Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2601.22874 |