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About approximation of integer factorization problem by the combination fixed-point iteration method and Bayesian rounding for quantum cryptography

机译:关于整数分解问题的近似定点迭代方法和贝叶斯舍入对量子密码学

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We describe the possibility of employing the special case of the 3-SAT problem stemming from the well known integer factorization problem for the quantum cryptography. It is known, that for every instance of our 3-SAT setting the given 3-CNF is satisfiable by a unique truth assignment, and the goal is to find this assignment. Since the complexity status of the factorization problem is still undefined, development of approximation algorithms and heuristics adopts interest of numerous researchers. One of promising approaches to construction of approximation techniques is based on real-valued relaxation of the given 3-CNF followed by minimizing of the appropriate differentiable loss function, and subsequent rounding of the fractional minimizer obtained. Actually, algorithms developed this way differ by the rounding scheme applied on their final stage. We propose a new rounding scheme based on Bayesian learning. The article shows that the proposed method can be used to determine the security in quantum key distribution systems. In the quantum distribution the Shannon rules is applied and the factorization problem is paramount when decrypting secret keys.
机译:我们描述了在众所周知的整数分解问题中雇用3-SAT问题的特殊情况的可能性。众所周知,对于我们的3-SAT设置的每个例子,给定的3-CNF通过独特的事实分配是满意的,并且目标是找到此作业。由于分解问题的复杂性状态仍未确定,因此近似算法和启发式的发展采用众多研究人员的兴趣。承诺逼近技术的承诺方法之一是基于给定的3-CNF的实值松弛,然后最小化适当的可分辨率损失功能,并随后获得的分数最小化器的舍入。实际上,这种方式开发的算法因其最终阶段应用的舍入方案而异。我们提出了一种基于贝叶斯学习的新舍入计划。本文表明,所提出的方法可用于确定量子密钥分配系统中的安全性。在量子分布中,在解密秘密密钥时,应用香农规则,分解问题是至关重要的。

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