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.
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