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.
@article{arxiv.2011.05062,
title = {Quantum Spike Neural Network},
author = {Yanhu Chen and Hongxiang Guo and Cen Wang and Xiong Gao and Jian Wu},
journal= {arXiv preprint arXiv:2011.05062},
year = {2020}
}