Pattern recognition with superconducting wirelet neurons

Abstract

Neuromorphic computing aims to reproduce the energy efficiency and adaptability of biological intelligence in hardware. Superconducting devices are an attractive platform due to their ultra-low dissipation and fast switching dynamics. Here we introduce a shunted superconducting wirelet as an artificial neuron, representing the simplest possible superconducting neuron implementation. This minimal design, a single superconducting channel with a resistive shunt, enables straightforward fabrication, electronic control, and high scalability. The neuron exhibits spiking voltage behavior driven by the interplay of resistive switching and relaxation, with key properties such as threshold, firing frequency, and refractory time tunable via applied current, temperature, and shunt resistance. We further show that the resulting temporal voltage signals can be incorporated into a training algorithm to achieve accurate pattern recognition, demonstrating suitability for neuromorphic tasks. Finally, we discuss on-chip training using similar wirelets with gated synaptic weights, establishing a scalable, energy-efficient building block for cryogenic artificial intelligence hardware, integrable with other emergent superconducting technologies.