Classical and Quantum Physical Reservoir Computing for Onboard Artificial Intelligence Systems: A Perspective
<|reference_start|>Classical and Quantum Physical Reservoir Computing for Onboard Artificial Intelligence Systems: A Perspective: 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.<|reference_end|>
@article{abbas2024classical,
    title={Classical and Quantum Physical Reservoir Computing for Onboard
  Artificial Intelligence Systems: A Perspective},
    author={A. H. Abbas, Hend Abdel-Ghani and Ivan S. Maksymov},
    journal={arXiv preprint arXiv:2407.04717},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.04717},
    primaryClass={cs.ET cs.AI cs.NE cs.RO nlin.CD physics.flu-dyn quant-ph}
}