Quantum Reservoir Computing and Its Potential Applications

讲演择要：
Amidst the rapid advancements in experimental technology, noise-intermediate-scale quantum (NISQ) devices have become increasingly progra妹妹able, offering versatile opportunities to leverage quantum computational advantage. We explore the intricate dynamics of progra妹妹able 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.

讲演人简介：
李晓鹏，复旦年夜学物理系传授。2008年本科结业于中国迷信技能年夜学，2013年正在美国匹兹堡年夜学得到博士学位。2013-2016年正在马里兰年夜学从事博士后研究，2016底参加复旦年夜学物理系任青年研究员，2017年入选海外高档次人材规划，2019年提升正传授，2020年起正在上海期智研究院专任良好迷信家, 2022年入选复旦年夜学青年谢希德传授，2024年获上海市青年五四奖章。首要从事量子摹拟与量子计较算法等方面的研究。