基于GPU的Shor分解算法加速
Accelerating Shor's Factorization Algorithm on GPUs
摘要：
Shor的量子算法对于密码学来说非常重要，因为它可以比经典算法更快地分解大数。在这项研究中，我们实现了一个模拟器Shor的量子算法的图形处理器单元（GPU），并比较我们的结果与液体-这是微软的量子模拟平台-和两个经典的CPU实现。我们评估了10个基准测试，用于将我们的GPU实现与Liquid和单核实现进行比较。分析表明，GPU的矢量运算更适合于Shor量子算法。我们的GPU内核函数是计算受限的，因为块中的所有线程都到达状态向量的相同元素。我们的实现比单核算法有$52.5\times$的加速，比Liquid有$20.5\times$的加速。
英文摘要：
Shor's quantum algorithm is very important for cryptography, since it can factor large numbers much faster than classical algorithms. In this study, we implement a simulator for Shor's quantum algorithm on graphic processor units (GPU) and compare our results with Liquid -which is Microsoft quantum simulation platform- and two classical CPU-implementations. We evaluate 10 benchmarks for comparing our GPU implementation with Liquid and single-core implementation. The analysis shows that GPU vector operations is more suitable for Shor's quantum algorithm. Our GPU kernel function is compute-bound, due to all threads in a block reach to the same element of the state vector. Our implementation has $52.5\times$ speedup over single-core algorithm and $20.5\times$ speedup over Liquid.
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。
