分布式精确多目标量子搜索算法
Distributed exact multi-objective quantum search algorithm
AI总结：
针对多目标量子搜索，本文设计两种分布式迭代算子并提出两种新的分布式Grover算法，可减少量子比特需求，其中一种实现精确搜索。
摘要：
多目标搜索是指在非结构化数据库中搜索若干目标中的任意一个。Grover算法在多目标搜索中相比经典算法具有二次加速。Grover算法中的迭代算子是一个关键要素，并在振幅放大中发挥重要作用。本文设计了两种分布式迭代算子，由此得到两种新的分布式Grover算法，它们具有以下优势：（1）与Grover算法以及Long提出的改进Grover算法相比，我们的分布式算法需要更少的量子比特；（2）与Qiu等人提出的分布式Grover算法相比，我们的其中一种分布式算法是精确的。当然，作为代价，我们的两种分布式算法都需要大量量子通信，并涉及许多更复杂的酉算子，但在含噪中等规模量子（NISQ）时代，它们在物理可实现性方面仍可能具有一定优势。
英文摘要：
Multi-objective search means searching for any one of several objectives in an unstructured database. Grover's algorithm has quadratic acceleration in multi-objection search than classical ones. Iterated operator in Grover's algorithm is a key element and plays an important role in amplitude amplification. In this paper, we design two distributed iterated operators and therefore two new distributed Grover's algorithms are obtained with the following advantages: (1) Compared to Grover's algorithm and the modified Grover's algorithm by Long, our distributed algorithms require fewer qubits; (2) Compared to the distributed Grover's algorithm proposed by Qiu et al., one of our distributed algorithms is exact. Of course, both our distributed algorithms require quite quantum communication and involve a number of more complicated unitary operators as cost, but there still may have certain advantage of physical realizability in the Noisy Intermediate-Scale Quantum (NISQ) era.
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。
