报告摘要：
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. 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.
报告人简介：
李晓鹏，复旦大学物理系教授。2008年本科毕业于中国科学技术大学，2013年在美国匹兹堡大学获得博士学位。2013-2016年在马里兰大学从事博士后研究，2016底加入复旦大学物理系任青年研究员，2017年入选海外高层次人才计划，2019年晋升正教授，2020年起在上海期智研究院兼任杰出科学家, 2022年入选复旦大学青年谢希德教授，2024年获上海市青年五四奖章。主要从事量子模拟与量子计算算法等方面的研究。