Quantum 2025 – scientific programme

Parts | Days | Selection | Search | Updates | Downloads | Help

MON: Monday Contributed Sessions

MON 4: DPG Promotionskolleg Next Generation Computing

MON 4.5: Talk

Monday, September 8, 2025, 15:15–15:30, ZHG004

Robustly optimal dynamics for active matter reservoir computing — •Mario U. Gaimann and Miriam Klopotek — Stuttgart Center for Simulation Science (SimTech), Cluster of Excellence EXC 2075, University of Stuttgart, Germany

Information processing abilities of active matter are studied in the reservoir computing (RC) paradigm to infer the future state of a chaotic signal. We uncover an exceptional regime of agent dynamics that has been overlooked previously. It appears robustly optimal for performance under many conditions, thus providing valuable insights into computation with physical systems more generally. The key to forming effective mechanisms for information processing appears in the system’s intrinsic relaxation abilities. These are probed without actually enforcing a specific inference goal. The dynamical regime that achieves optimal computation is located just below a critical damping threshold, involving a relaxation with multiple stages, and is readable at the single-particle level. At the many-body level, it yields substrates robustly optimal for RC across varying physical parameters and inference tasks. A system in this regime exhibits a strong diversity of dynamic mechanisms under highly fluctuating driving forces. Correlations of agent dynamics can express a tight relationship between the responding system and the fluctuating forces driving it. As this model is interpretable in physical terms, it facilitates re-framing inquiries regarding learning and unconventional computing with a fresh rationale for many-body physics out of equilibrium. Reference: Gaimann, M. U., & Klopotek, M. (2025), arXiv:2505.05420.

Keywords: Reservoir Computing; Active Matter; Analog Computation; Machine Learning; Next-Generation Computing