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Configured Quantum Reservoir Computing for Multi-Task Machine Learning

Computer Science

Configured Quantum Reservoir Computing for Multi-Task Machine Learning

W. Xia, J. Zou, et al.

This groundbreaking research by Wei Xia, Jie Zou, Xingze Qiu, Feng Chen, Bing Zhu, Chunhe Li, Dong-Ling Deng, and Xiaopeng Li reveals how programmable noise-intermediate-scale quantum devices can revolutionize quantum reservoir computing, achieving remarkable success in complex tasks and outperforming classical methods through quantum coherence.

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Playback language: English
Abstract
This paper explores the use of programmable noise-intermediate-scale quantum (NISQ) devices for quantum reservoir computing (QRC). A genetic algorithm configures the quantum reservoir dynamics, significantly improving learning performance. A single configured quantum reservoir successfully learns multiple complex tasks, including modeling gene regulatory networks, fractional-order Chua's circuits, and predicting foreign exchange (FX) market rates, outperforming classical reservoir computing in all cases. The quantum advantage is attributed to quantum coherence within the reservoir.
Publisher
Nature
Published On
Apr 03, 2023
Authors
Wei Xia, Jie Zou, Xingze Qiu, Feng Chen, Bing Zhu, Chunhe Li, Dong-Ling Deng, Xiaopeng Li
Tags
quantum reservoir computing
NISQ devices
genetic algorithm
quantum coherence
learning performance
gene regulatory networks
fractional-order circuits

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