| （英） |
Reservoir Computing (RC) is a machine learning method that aims to achieve both high learning performance and low learning cost, which are usually in a trade-off relationship. Among RC, those that utilize quantum systems are called Quantum RC (QRC). The nonlinearity of quantum reservoirs has conventionally been realized by relatively simple dynamics, such as measurement. However, the optimal nonlinearity for learning should lie between deterministic and random systems. In this study, we aim to improve the performance of QRC by controlling the complexity of the system through chaotic parameters. As a quantum reservoir, we employ the Heisenberg XXZ spin chain, known for its compatibility with quantum computers. |