Setting conditions for enhancing task accuracy in reservoir computing using superconductors

Abstract

Reservoir computing using nonlinear vortex dynamics in type-II superconductors enables low-power time series processing, though accuracy has been limited. To examine performance factors, 2D time-dependent Ginzburg-Landau simulations were conducted with varied pin density, pinning strength, and temperature. Pinning was controlled via local α values, and temperature via bulk α. Input current and output electric field formed the input-output pair, evaluated by NARMA2 accuracy and memory capacity. Results showed optimal performance at moderate pinning and low temperatures. Irregular responses at low temperature were linked to enhanced vortex-pin interactions, offering design insights for high-precision superconducting reservoir hardware.