New algorithms for prime factorization that outperform the existing ones or take advantage of particular properties of the prime factors can have a practical impact on present implementations of cryptographic algorithms that rely on the complexity of factorization. Currently used keys are chosen on the basis of the present algorithmic knowledge and, thus, can potentially be subject to future breaches. For this reason, it is worth to investigate new approaches which have the potentiality of giving a computational advantage. The problem has also relevance in quantum computation, as an efficient quantum algorithm for prime factorization already exists. Thus, better classical asymptotic complexity can provide a better understanding of the advantages offered by quantum computers. In this paper, we reduce the factorization problem to the search of points of parametrizable varieties, in particular curves, over finite fields. The varieties are required to have an arbitrarily large number of intersection points with some hypersurface over the base field. For a subexponential or poly- nomial factoring complexity, the number of parameters have to scale sublinearly in the space dimension n and the complexity of computing a point given the parameters has to be subexponential or polynomial, respectively. We outline a procedure for building these varieties, which is illustrated with two constructions. In one case, we show that there are varieties whose points can be evaluated efficiently given a number of parameters not greater than n/2. In the other case, the bound is dropped to n/3. Incidentally, the first construction resembles a kind of retro-causal model. Retro-causality is considered one possible explanation of quantum weirdness.
翻译:超越现有参数或利用主要因素特定特性的质因子化新算法, 可能会对目前使用依赖于因素化复杂性的加密算法产生实际影响。 目前使用的键是根据目前的算法知识选择的, 因此, 有可能在未来发生破坏。 因此, 值得调查具有提供计算优势潜力的新方法。 这个问题在量子计算中也有相关性, 因为质因子化的高效量算法已经存在。 因此, 更典型的单质复杂性可以使人们更好地了解量子计算机提供的优势。 在本文件中, 我们减少因子化问题, 以搜索可蛋白化品种的点, 特别是曲线, 从而在限定字段中选择当前使用的键。 品种必须拥有任意大量的交叉点, 在基础字段上具有某种超高表层。 对于分解或多量量因子计算的复杂性, 参数的数量在空间维度上可能进行子线性调整, 以及计算某个参数的复杂程度, 在本文中, 一个直线性参数的精度参数是分解的精度, 。