蛋白质序列比较中的快速量子搜索算法-量子生物计算
Fast Quantum Search Algorithms in Protein Sequence Comparison - Quantum Biocomputing
摘要：
量子搜索算法被认为是在生物计算中的蛋白质序列比较的上下文中。给定一个长度为m（即m个残基）的样本蛋白质序列，所考虑的问题是在包含N个残基的大型数据库中找到最佳匹配。最初，Grover的量子搜索算法应用于一个简单的说明性情况-即数据库在m量子位寄存器的2^m个基态上形成完整的状态集，因此已知包含感兴趣的确切序列。这个例子显式地演示了经典O（N）要求的典型O（sqrt{N}）加速。然后提出了一种算法的（更现实的）情况下，数据库中可能包含重复序列，并可能不一定包含一个精确的匹配的样品序列。在最小化样本序列和数据库序列之间的汉明距离方面，该算法通过采用Grover算法的扩展在O（sqrt{N}）步中找到最佳比对，这是由于Boyer、Bohard、Hoyer和Tapp针对匹配数量不是先验已知的情况。
英文摘要：
Quantum search algorithms are considered in the context of protein sequence comparison in biocomputing. Given a sample protein sequence of length m (i.e m residues), the problem considered is to find an optimal match in a large database containing N residues. Initially, Grover's quantum search algorithm is applied to a simple illustrative case - namely where the database forms a complete set of states over the 2^m basis states of a m qubit register, and thus is known to contain the exact sequence of interest. This example demonstrates explicitly the typical O(sqrt{N}) speedup on the classical O(N) requirements. An algorithm is then presented for the (more realistic) case where the database may contain repeat sequences, and may not necessarily contain an exact match to the sample sequence. In terms of minimizing the Hamming distance between the sample sequence and the database subsequences the algorithm finds an optimal alignment, in O(sqrt{N}) steps, by employing an extension of Grover's algorithm, due to Boyer, Brassard, Hoyer and Tapp for the case when the number of matches is not a priori known.
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。
