# Classical and Quantum Physical Reservoir Computing for Onboard Artificial Intelligence Systems: A Perspective arXiv: https://arxiv.org/abs/2407.04717 ยท HTML: https://mlsanity.com/paper/2407.04717/ ## Key facts - Cited by 9 papers in the map (data through 14 Oct 2026). - Builds on 25 of its 218 references that are in the map. - First posted on arXiv 14 Jun 2024. ## Builds on (most cited first, 25 of 25) - [Next Generation Reservoir Computing](https://mlsanity.com/paper/2106.07688/) (2021) - [Photonics for artificial intelligence and neuromorphic computing](https://mlsanity.com/paper/2011.00111/) (2020) - [A Systematic Review of Green AI](https://mlsanity.com/paper/2301.11047/) (2023) - [Physical reservoir computing -- An introductory perspective](https://mlsanity.com/paper/2005.00992/) (2020) - [A Long Short-Term Memory for AI Applications in Spike-based Neuromorphic Hardware](https://mlsanity.com/paper/2107.03992/) (2021) - [Systematic Literature Review: Quantum Machine Learning and its applications](https://mlsanity.com/paper/2201.04093/) (2022) - [Temporal Information Processing on Noisy Quantum Computers](https://mlsanity.com/paper/2001.09498/) (2020) - [A Survey on Reservoir Computing and its Interdisciplinary Applications Beyond Traditional Machine Learning](https://mlsanity.com/paper/2307.15092/) (2023) - [Theory of neuromorphic computing by waves: machine learning by rogue waves, dispersive shocks, and solitons](https://mlsanity.com/paper/1912.07044/) (2019) - [Analogue and Physical Reservoir Computing Using Water Waves](https://mlsanity.com/paper/2306.09095/) (2023) - [Learning Nonlinear Input-Output Maps with Dissipative Quantum Systems](https://mlsanity.com/paper/1901.01653/) (2019) - [A perspective on physical reservoir computing with nanomagnetic devices](https://mlsanity.com/paper/2212.04851/) (2022) - [Harnessing disordered quantum dynamics for machine learning](https://mlsanity.com/paper/1602.08159/) (2016) - [Data-Driven Denoising of Stationary Accelerometer Signals](https://mlsanity.com/paper/2206.05937/) (2022) - [Deep Spatiotemporal Models for Robust Proprioceptive Terrain Classification](https://mlsanity.com/paper/1804.00736/) (2018) - [Quantum-Inspired Neural Network Model of Optical Illusions](https://mlsanity.com/paper/2312.03447/) (2023) - [Reservoir Computing Using Measurement-Controlled Quantum Dynamics](https://mlsanity.com/paper/2403.01024/) (2024) - [Neural Echo State Network using oscillations of gas bubbles in water](https://mlsanity.com/paper/2112.11592/) (2021) - [Learning Long-Range Perception Using Self-Supervision from Short-Range Sensors and Odometry](https://mlsanity.com/paper/1809.07207/) (2018) - [A New Paradigm of Reservoir Computing Exploiting Hydrodynamics](https://mlsanity.com/paper/2302.01978/) (2023) - [Reservoir computing based on solitary-like waves dynamics of film flows: a proof of concept](https://mlsanity.com/paper/2303.01801/) (2023) - [Physical Reservoir Computing Enabled by Solitary Waves and Biologically-Inspired Nonlinear Transformation of Input Data](https://mlsanity.com/paper/2402.03319/) (2024) - [Passive frustrated nanomagnet reservoir computing](https://mlsanity.com/paper/2103.09353/) (2021) - [Photonic reservoir computing enabled by stimulated Brillouin scattering](https://mlsanity.com/paper/2302.07698/) (2023) - [Underwater Acoustic Communication Channel Modeling using Reservoir Computing](https://mlsanity.com/paper/2205.14856/) (2022) ## Built on by (most cited first, 9 of 9) - [Quantum-Cognitive Neural Networks: Assessing Confidence and Uncertainty with Human Decision-Making Simulations](https://mlsanity.com/paper/2412.08010/) (2024) - [Full-Precision and Ternarised Neural Networks with Tunnel-Diode Activation Functions: Computing and Physics Perspectives](https://mlsanity.com/paper/2503.04978/) (2025) - [Reservoir Computing with a Single Oscillating Gas Bubble: Emphasizing the Chaotic Regime](https://mlsanity.com/paper/2504.07221/) (2025) - [TunnElQNN: A Hybrid Quantum-classical Neural Network for Efficient Learning](https://mlsanity.com/paper/2505.00933/) (2025) - [Transforming Traditional Neural Networks into Neuromorphic Quantum-Cognitive Models: A Tutorial with Applications](https://mlsanity.com/paper/2503.07681/) (2025) - [Resonant-Tunnelling Diode Reservoir Computing System for Image Recognition](https://mlsanity.com/paper/2507.15158/) (2025) - [Quantum-Cognitive Tunnelling Neural Networks for Military-Civilian Vehicle Classification and Sentiment Analysis](https://mlsanity.com/paper/2507.18645/) (2025) - [Vestibular reservoir computing](https://mlsanity.com/paper/2604.09943/) (2026) - [Quantum Reservoir Computing: Recent Advances and Future Directions](https://mlsanity.com/paper/2607.18552/) (2026) ## Abstract (arXiv) Artificial intelligence (AI) systems of autonomous systems such as drones, robots and self-driving cars may consume up to 50% of total power available onboard, thereby limiting the vehicle's range of functions and considerably reducing the distance the vehicle can travel on a single charge. Next-generation onboard AI systems need an even higher power since they collect and process even larger amounts of data in real time. This problem cannot be solved using the traditional computing devices since they become more and more power-consuming. In this review article, we discuss the perspectives of development of onboard neuromorphic computers that mimic the operation of a biological brain using nonlinear-dynamical properties of natural physical environments surrounding autonomous vehicles. Previous research also demonstrated that quantum neuromorphic processors (QNPs) can conduct computations with the efficiency of a standard computer while consuming less than 1% of the onboard battery power. Since QNPs is a semi-classical technology, their technical simplicity and low, compared with quantum computers, cost make them ideally suitable for application in autonomous AI system. Providing a perspective view on the future progress in unconventional physical reservoir computing and surveying the outcomes of more than 200 interdisciplinary research works, this article will be of interest to a broad readership, including both students and experts in the fields of physics, engineering, quantum technologies and computing.