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arXiv 2309.14366quant-phcs.AIcs.ETcs.LG
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一种面向非理想训练集的基于酉权重的单次迭代量子感知机算法

A Unitary Weights Based One-Iteration Quantum Perceptron Algorithm for Non-Ideal Training Sets

Jiangsu Engineering Center of Network Monitoring(江苏省网络监控工程中心);Nanjing University of Information Science and Technology(南京信息工程大学);School of Computer and Software, Nanjing University of Information Science and Technology(南京信息工程大学计算机与软件学院);School of Mathematics and Computing Science, Guilin University of Electronic Technology(桂林电子科技大学数学与计算科学学院);College of Computer Science and Information Technology, Guangxi Normal University(广西师范大学计算机科学与信息工程学院)

Wenjie Liu, Peipei Gao, Yuxiang Wang, Wenbin Yu, Maojun Zhang

AI总结:

针对欠完备或过完备训练集,本文提出通过奇异值分解构造酉权重矩阵的单次迭代量子感知机算法,并在多种量子门与复合门上验证其精确性和优势。

摘要:

为了解决非理想训练集(即欠完备或过完备集合)的问题并实现单次迭代学习,本文提出了一种基于酉权重的新型高效量子感知机算法。该算法对由训练集得到的总权重矩阵进行奇异值分解,使权重矩阵成为酉矩阵。对量子门 {H、S、T、CNOT、Toffoli、Fredkin} 的示例验证表明,本算法能够在一次迭代内精确实现任意量子门。本算法与其他量子感知机算法的性能比较,证明了其在适用性、准确性和可用性方面的优势。为进一步验证本算法的适用性,文中还说明了一个由若干基本量子门组成的量子复合门。

英文摘要:

In order to solve the problem of non-ideal training sets (i.e., the less-complete or over-complete sets) and implement one-iteration learning, a novel efficient quantum perceptron algorithm based on unitary weights is proposed, where the singular value decomposition of the total weight matrix from the training set is calculated to make the weight matrix to be unitary. The example validation of quantum gates {H, S, T, CNOT, Toffoli, Fredkin} shows that our algorithm can accurately implement arbitrary quantum gates within one iteration. The performance comparison between our algorithm and other quantum perceptron algorithms demonstrates the advantages of our algorithm in terms of applicability, accuracy, and availability. For further validating the applicability of our algorithm, a quantum composite gate which consists of several basic quantum gates is also illustrated.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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