Numerical Study on Physical Reservoir Computing With Josephson Junctions
Kohki Watanabe, Yoshinao Mizugaki, Satoshi Moriya, Hideaki Yamamoto, Taro Yamashita, Shigeo Sato · IEEE Transactions on Applied Superconductivity · 2024
In this study, we propose reservoir computing, a novel machine learning framework, utilizing the Josephson transmission line (JTL) as a promising hardware candidate to realize low-power and high-speed computation. A two-dimensional JTL circuit is designed as a reservoir in accordance with a previous study, and digit image recognition tasks are demonstrated with the circuit. The simulation results show that noisy digit images are successfully classified with an accuracy of$\text{80}\%$at a rate of$\text{50}\, {\mathbf{Gpixels/s}}$. The power consumption of this system is estimated to be$\text{12.8}\,\mu \text{W}$, which is comparable to that of spin reservoirs and optical reservoirs. Thus, we confirm that the proposed system has great potential for application in machine learning and AI processing.