Numerical Study on Physical Reservoir Computing With Josephson Junctions

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 80% at a rate of 50Gpixels/s . The power consumption of this system is estimated to be 12.8 mu 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.