Wang Zhaobin, Xu Minzhe, Zhang Yaonan
School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China; National Cryosphere Desert Data Center, Lanzhou 730000, China.
School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China.
Neural Netw. 2022 Aug;152:105-117. doi: 10.1016/j.neunet.2022.04.007. Epub 2022 Apr 18.
Artificial neural network has been fully developed in recent years, but as the size of the network grows, the required computing power also grows rapidly. In order to take advantage of the parallel computing of quantum computing to solve the difficulties of large computation in neural network, quantum neural network was proposed. In this paper, based on the pulse coupled neural network (PCNN), quantum pulse coupled neural network (QPCNN) is proposed. In this model, the basic quantum logic gates are utilized to form quantum operation modules, such as quantum full adder, quantum multiplier, and quantum comparator. A quantum image convolution operation applicable to QPCNN is designed employing quantum full adders and neighborhood preparation module. And these modules are employed to complete the operations required for QPCNN. And based on QPCNN, an quantum image segmentation is designed. Meanwhile, the effectiveness of QPCNN is proved by simulation experiments, and the complexity analysis shows that QPCNN has exponential speedup compared with classical PCNN.
近年来，人工神经网络得到了充分发展，但随着网络规模的增大，所需的计算能力也迅速增长。为了利用量子计算的并行计算来解决神经网络中大规模计算的难题，人们提出了量子神经网络。本文基于脉冲耦合神经网络（PCNN），提出了量子脉冲耦合神经网络（QPCNN）。在该模型中，利用基本量子逻辑门形成量子运算模块，如量子全加器、量子乘法器和量子比较器。采用量子全加器和邻域制备模块设计了一种适用于QPCNN的量子图像卷积运算。并且利用这些模块完成QPCNN所需的运算。在此基础上，设计了一种量子图像分割方法。同时，通过仿真实验验证了QPCNN的有效性，复杂度分析表明，与经典PCNN相比，QPCNN具有指数级加速。
