发表机构
University of Science and Technology of China（中国科学技术大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种35输入单输出的量子相位神经网络，通过量子相位编码与旋转门实现字母识别，并推导自适应学习算法，在Qiskit上验证，有效规避指数墙问题。
AI 中文摘要
本文提出了一种具有35个输入和单输出的量子相位神经网络（QPNN），用于识别由7×5像素组成的英文字母'A'和'B'。所提出的QPNN的每个节点具有0或1的输入。通过归一化和量子相位编码，每组包含35个0或1数字输入的集合被编码为由35个量子相位描述的量子叠加态。然后，使用量子旋转门引入可调相位，并使用受控非门纠缠两个输入之间的相对相位。推导了网络的输入与输出之间的关系。本文还推导了具有自适应学习率的相位权重学习训练算法。设计了识别概率为1的解，并且所有零误差的可调相位解的解析表达式不是唯一的。实验性能在Qiskit平台上得到了验证。本文利用相位驱动将概率计算转化为网络中具有可调旋转角度的解析多项式函数，避免了生成所有2^35个复指数振幅的瓶颈，为解决“指数墙”问题提供了一种有效途径，为多输入识别问题的实际应用提供了一种新的实现方案。
英文摘要
A 35 input/single output quantum phase neural network (QPNN) is proposed to recognize the English letters' A 'and' B 'composed 7*5 of pixels. Each node of the proposed QPNN has the input with 0 or 1. Through normalization and quantum phase encoding, each set of 35 with 0 or 1 digital inputs is encoded into a quantum superposition state described by 35 quantum phases. Then, a quantum rotation gate is used to introduce adjustable phase, and a controlled NOT gate is used to entangle the relative phase between the two inputs. The relationship between the input and output of the network is derived. This paper also derived the phase weight learning training algorithm with adaptive learning rate. The solutions with the recognition probability of 1 is designed, and the analytical expressions for all adjustable phase solutions with zero errors are not unique. The experimental performance is verified on the Qiskit platform. This paper uses phase drive to convert the probability calculation into an analytical polynomial function with adjustable rotation angles in the network, avoiding the bottleneck of generating all 2^35 complex exponential amplitudes and providing an effective way to solve the "exponential wall" problem, which provides a new implementation solution for the practical application of multi-input recognition problems.
Comments6 pages
