# Superconducting Neuromorphic Computing Using Quantum Phase-Slip Junctions

## TL;DR

This paper explores superconducting neuromorphic circuits using quantum phase-slip junctions, demonstrating neuron and synaptic components with simulations and discussing their potential for energy-efficient, high-speed computing.

## Contribution

It introduces superconducting neuromorphic components based on QPSJs and MJJs, with simulation results and energy/delay estimates, advancing quantum-based neuromorphic hardware.

## Key findings

- QPSJ-based neurons and synapses can operate with low energy dissipation.
- Simulations show feasible operation speeds for superconducting neuromorphic circuits.
- The study provides initial estimates for energy efficiency and speed of QPSJ-based components.

## Abstract

Superconducting circuits based on quantum phase-slip junctions (QPSJs) can conduct quantized charge pulses, which naturally resemble action potentials generated by biological neurons. A corresponding synaptic circuit, which works as a weighted connection between two neurons, can also be realized by circuits comprised of QPSJs and magnetic Josephson junctions (MJJs) as a means of charge modulation for quantized charge propagation. In this paper, we present basic neuromorphic components such as neuron and synaptic circuits based on superconducting QPSJs and MJJs. Using a SPICE model developed for QPSJs, neuron and synaptic circuits have been simulated in WRSPICE to demonstrate possible operation. We provide estimates for QPSJ energy dissipation and operation speed based on calculations using simple models. The challenges for implementation of this technology are also briefly discussed.