Reservoir Computing with a single Josephson junction

TL;DR

This paper demonstrates that a single Josephson junction can serve as an effective reservoir computing device, leveraging its nonlinear dynamics for information processing without delay loops, promising ultrafast hardware applications.

Contribution

It introduces the novel use of a single Josephson junction as a reservoir computing substrate, analyzing its performance and input modulation methods through numerical simulations.

Findings

Optimal performance occurs in a stable yet responsive dynamical regime.

The Josephson junction exhibits sufficient memory for chaotic time series prediction.

Continuous modulation input masking is compatible with practical implementations.

Abstract

Physical reservoir computing exploits the nonlinear dynamics of a physical system to perform information processing tasks. Josephson junctions (JJs), as nonlinear superconducting devices with rich dynamical behavior, represent promising yet relatively unexplored candidates for reservoir computing. In this work, we demonstrate for the first time that a single Josephson junction can be employed as a reservoir computing substrate without the use of an explicit delay loop. Using numerical simulations, we analyze the reservoir performance in different dynamical regimes and show that optimal performance is achieved when the JJ operates in a stable yet responsive regime. Despite the absence of delayed feedback, the JJ exhibits sufficient memory through its intrinsic dynamics to achieve good performance on a chaotic time series prediction task. In addition, we explore an alternative input masking approach based on continuous modulation, highlighting its compatibility with practical implementations. These results establish Josephson junctions as a viable and efficient platform for reservoir computing and open the way to ultrafast, low-dissipation hardware realizations.