Quantum Spike Neural Network

TL;DR

This paper introduces quantum spike neural networks (SNNs), providing theoretical analysis, complexity proofs, and methods to enhance success probability, demonstrating their potential for efficient pattern recognition tasks.

Contribution

It proposes a new quantum SNN model with detailed mathematical proof and methods to improve its success probability, advancing quantum neural network research.

Findings

Quantum SNNs have log-polynomial complexity in data dimension.

The success probability of quantum SNNs can be increased to nearly 100%.

Quantum SNNs perform well in real-world pattern recognition tasks.

Abstract

Utilizing quantum computers to deploy artificial neural networks (ANNs) will bring the potential of significant advancements in both speed and scale. In this paper, we propose a kind of quantum spike neural networks (SNNs) as well as comprehensively evaluate and give a detailed mathematical proof for the quantum SNNs, including its successful probability, calculation accuracy, and algorithm complexity. The proof shows the quantum SNNs' computational complexity that is log-polynomial in the data dimension. Furthermore, we provide a method to improve quantum SNNs' minimum successful probability to nearly 100%. Finally, we present the good performance of quantum SNNs for solving pattern recognition from the real-world.