基于量子机器学习模型的安全量子密钥分发最优参数预测

B. Babu, K. Bhargavi, K. Subramanya
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引用次数: 1

摘要

量子计算的出现给经典密码技术的成功运行带来了威胁。为了在有限的时间间隔内进行量子密钥分发,需要对光子状态进行估计并对波动进行统计分析。使用暴力和局部搜索方法进行参数优化的计算量很大,即使对于较小的连接也是不可行的解决方案。因此,利用具有自学习能力的量子机器学习模型来预测量子密钥分发的最优参数是有用的。本章讨论了一些量子机器学习模型及其架构、优缺点。量子卷积神经网络(QCNN)和量子粒子群优化(QPSO)在量子密钥分配方面的性能优于其他所有讨论的量子机器学习模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimal Parameter Prediction for Secure Quantum Key Distribution Using Quantum Machine Learning Models
The advent of quantum computing is bringing threats to successful operations of classical cryptographic techniques. To conduct quantum key distribution (QKD) in a finite time interval, there is a need to estimate photon states and analyze the fluctuations statistically. The use of brute force and local search methods for parameter optimization are computationally intensive and becomes an infeasible solution even for smaller connections. Therefore, the use of quantum machine learning models with self-learning ability is useful in predicting the optimal parameters for quantum key distribution. This chapter discusses some of the quantum machine learning models with their architecture, advantages, and disadvantages. The performance of quantum convoluted neural network (QCNN) and Quantum Particle Swarm Optimization (QPSO) towards QKD is found to be good compared to all the other quantum machine learning models discussed.
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