# Physical reservoir computing -- An introductory perspective

arXiv: https://arxiv.org/abs/2005.00992 · HTML: https://mlsanity.com/paper/2005.00992/

## Key facts

- Cited by 58 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 May 2020.

## Built on by (most cited first, 30 of 58)

- [Training of Physical Neural Networks](https://mlsanity.com/paper/2406.03372/) (2024)
- [Analogue and Physical Reservoir Computing Using Water Waves](https://mlsanity.com/paper/2306.09095/) (2023)
- [Biological neurons act as generalization filters in reservoir computing](https://mlsanity.com/paper/2210.02913/) (2022)
- [Learning Temporal Quantum Tomography](https://mlsanity.com/paper/2103.13973/) (2021)
- [Classical and Quantum Physical Reservoir Computing for Onboard Artificial Intelligence Systems: A Perspective](https://mlsanity.com/paper/2407.04717/) (2024)
- [Exploring the limits of multifunctionality across different reservoir computers](https://mlsanity.com/paper/2205.11375/) (2022)
- [Reservoir Computing Using Measurement-Controlled Quantum Dynamics](https://mlsanity.com/paper/2403.01024/) (2024)
- [Extending echo state property for quantum reservoir computing](https://mlsanity.com/paper/2403.02686/) (2024)
- [Neural Echo State Network using oscillations of gas bubbles in water](https://mlsanity.com/paper/2112.11592/) (2021)
- [Learning Reservoir Dynamics with Temporal Self-Modulation](https://mlsanity.com/paper/2301.09235/) (2023)
- [Confabulation dynamics in a reservoir computer: Filling in the gaps with untrained attractors](https://mlsanity.com/paper/2505.04792/) (2025)
- [Harnessing Synthetic Active Particles for Physical Reservoir Computing](https://mlsanity.com/paper/2307.15010/) (2023)
- [Designing Chaotic Attractors: A Semi-supervised Approach](https://mlsanity.com/paper/2407.09545/) (2024)
- [Stochastic dynamics learning with state-space systems](https://mlsanity.com/paper/2508.07876/) (2025)
- [Transient Chaos in BERT](https://mlsanity.com/paper/2106.03181/) (2021)
- [Quantum Noise-Induced Reservoir Computing](https://mlsanity.com/paper/2207.07924/) (2022)
- [Quantum-Classical Hybrid Information Processing via a Single Quantum System](https://mlsanity.com/paper/2209.00497/) (2022)
- [Building Intelligence in the Mechanical Domain -- Harvesting the Reservoir Computing Power in Origami to Achieve Information Perception Tasks](https://mlsanity.com/paper/2302.05517/) (2023)
- [Learning unidirectional coupling using echo-state network](https://mlsanity.com/paper/2303.13562/) (2023)
- [Biomembrane-based Memcapacitive Reservoir Computing System for Energy Efficient Temporal Data Processing](https://mlsanity.com/paper/2305.12025/) (2023)
- [Universal Mechanical Polycomputation in Granular Matter](https://mlsanity.com/paper/2305.17872/) (2023)
- [Exploiting Chaotic Dynamics as Deep Neural Networks](https://mlsanity.com/paper/2406.02580/) (2024)
- [How noise affects memory in linear recurrent networks](https://mlsanity.com/paper/2409.03187/) (2024)
- [Quantum Reservoir Computing and Risk Bounds](https://mlsanity.com/paper/2501.08640/) (2025)
- [Multifunctional physical reservoir computing in soft tensegrity robots](https://mlsanity.com/paper/2507.21496/) (2025)
- [Growing Reservoirs with Developmental Graph Cellular Automata](https://mlsanity.com/paper/2508.08091/) (2025)
- [Echoes of the Past: A Unified Perspective on Fading memory and Echo States](https://mlsanity.com/paper/2508.19145/) (2025)
- [Self-Organising Memristive Networks as Physical Learning Systems](https://mlsanity.com/paper/2509.00747/) (2025)
- [Virtual reservoir acceleration for CPU and GPU: Case study for coupled spin-torque oscillator reservoir](https://mlsanity.com/paper/2312.01121/) (2023)
- [Thermodynamic limit in learning period three](https://mlsanity.com/paper/2405.08825/) (2024)

## Abstract (arXiv)

Understanding the fundamental relationships between physics and its information-processing capability has been an active research topic for many years. Physical reservoir computing is a recently introduced framework that allows one to exploit the complex dynamics of physical systems as information-processing devices. This framework is particularly suited for edge computing devices, in which information processing is incorporated at the edge (e.g., into sensors) in a decentralized manner to reduce the adaptation delay caused by data transmission overhead. This paper aims to illustrate the potentials of the framework using examples from soft robotics and to provide a concise overview focusing on the basic motivations for introducing it, which stem from a number of fields, including machine learning, nonlinear dynamical systems, biological science, materials science, and physics.