  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.
