一种用于成对比对序列的线性时间量子算法
A Linear Time Quantum Algorithm for Pairwise Sequence Alignment
AI总结：
该研究将DNA序列比对问题映射为二维图路径搜索问题，结合收益计算预言机与Grover搜索算法，提出了可在线性时间内得到最优比对的量子算法，还能为非结构化搜索带来二次加速，优于经典确定性及现有随机算法。
摘要：
序列比对是为识别多条序列间的相似性而对生物序列进行比对的过程。本文提出了一种用于寻找DNA序列间最优比对的量子算法，其工作原理是将序列比对问题映射为通过二维图的路径搜索问题。该算法的转移过程会收敛到图上的一条固定路径，其核心是一种用于收益计算的预言机（oracle）。通过实现Grover搜索算法，本文提出的方法能够完成两条序列的比对，并在线性时间内找出最优比对结果，这是任何经典确定性算法都无法实现的。除此之外，该算法能够以确定性方式准确找出最优路径，可对任意非结构化搜索问题实现二次加速；相比之下，现有的随机算法经常得到次优比对结果，因此无法始终保证找到最优解。
英文摘要：
Sequence Alignment is the process of aligning biological sequences in order to identify similarities between multiple sequences. In this paper, a Quantum Algorithm for finding the optimal alignment between DNA sequences has been demonstrated which works by mapping the sequence alignment problem into a path-searching problem through a 2D graph. The transition, which converges to a fixed path on the graph, is based on a proposed oracle for profit calculation. By implementing Grover's search algorithm, our proposed approach is able to align a pair of sequences and figure out the optimal alignment within linear time, which hasn't been attained by any classical deterministic algorithm. In addition to that, the proposed algorithm is capable of quadratic speeding up to any unstructured search problem by finding out the optimal paths accurately in a deterministic manner, in contrast to existing randomized algorithms that frequently sort out the sub-optimal alignments, therefore, don't always guarantee of finding out the optimal solutions.
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。
