Configured quantum reservoir computing for multi-task machine learning

Wei Xia; Jie Zou; Xingze Qiu; Feng Chen; Bing Zhu; Chunhe Li; Dong-Ling Deng; Xiaopeng Li

doi:10.1016/j.scib.2023.08.040

Configured quantum reservoir computing for multi-task machine learning

Sci Bull (Beijing). 2023 Oct 30;68(20):2321-2329. doi: 10.1016/j.scib.2023.08.040. Epub 2023 Aug 26.

Authors

Wei Xia¹, Jie Zou¹, Xingze Qiu², Feng Chen³, Bing Zhu⁴, Chunhe Li⁵, Dong-Ling Deng⁶, Xiaopeng Li⁷

Affiliations

¹ State Key Laboratory of Surface Physics, Key Laboratory of Micro and Nano Photonic Structures (MOE), and Department of Physics, Fudan University, Shanghai 200433, China.
² State Key Laboratory of Surface Physics, Key Laboratory of Micro and Nano Photonic Structures (MOE), and Department of Physics, Fudan University, Shanghai 200433, China; School of Physics Science and Engineering, Tongji University, Shanghai 200092, China.
³ Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China.
⁴ Hong Kong and Shang Hai Banking Corporation Laboratory, Hong Kong and Shang Hai Banking Corporation Holdings PLC, Guangzhou 511458, China.
⁵ Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China; Shanghai Center for Mathematical Sciences and School of Mathematical Sciences, Fudan University, Shanghai 200433, China.
⁶ Center for Quantum Information, IIIS, Tsinghua University, Beijing 100084, China; Shanghai Qi Zhi Institute, AI Tower, Shanghai 200232, China.
⁷ State Key Laboratory of Surface Physics, Key Laboratory of Micro and Nano Photonic Structures (MOE), and Department of Physics, Fudan University, Shanghai 200433, China; Shanghai Qi Zhi Institute, AI Tower, Shanghai 200232, China; Shanghai Research Center for Quantum Sciences, Shanghai 201315, China. Electronic address: xiaopeng_li@fudan.edu.cn.

PMID: 37679257
DOI: 10.1016/j.scib.2023.08.040

Abstract

Amidst the rapid advancements in experimental technology, noise-intermediate-scale quantum (NISQ) devices have become increasingly programmable, offering versatile opportunities to leverage quantum computational advantage. Here we explore the intricate dynamics of programmable NISQ devices for quantum reservoir computing. Using a genetic algorithm to configure the quantum reservoir dynamics, we systematically enhance the learning performance. Remarkably, a single configured quantum reservoir can simultaneously learn multiple tasks, including a synthetic oscillatory network of transcriptional regulators, chaotic motifs in gene regulatory networks, and the fractional-order Chua's circuit. Our configured quantum reservoir computing yields highly precise predictions for these learning tasks, outperforming classical reservoir computing. We also test the configured quantum reservoir computing in foreign exchange (FX) market applications and demonstrate its capability to capture the stochastic evolution of the exchange rates with significantly greater accuracy than classical reservoir computing approaches. Through comparison with classical reservoir computing, we highlight the unique role of quantum coherence in the quantum reservoir, which underpins its exceptional learning performance. Our findings suggest the exciting potential of configured quantum reservoir computing for exploiting the quantum computation power of NISQ devices in developing artificial general intelligence.

Keywords: Configured quantum reservoir computing; Multi-task learning; Quantum advantage; Quantum coherence.