论文标题
量子峰神经网络
Quantum Spike Neural Network
论文作者
论文摘要
利用量子计算机部署人工神经网络（ANN）将带来速度和规模的重大进步的潜力。在本文中，我们提出了一种量子尖峰神经网络（SNN），并全面评估并为量子SNN提供详细的数学证明，包括其成功的概率，计算准确性和算法复杂性。该证明显示了数据维度的对数多项式的量子SNNS的计算复杂性。此外，我们提供了一种将量子SNN的最低成功概率提高到近100％的方法。最后，我们介绍了量子SNN的良好性能，以解决现实世界中的模式识别。
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.
