基于不相交素蕴涵的叠加量子态初始化

David J. Rosenbaum, M. Perkowski
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引用次数: 5

摘要

初始化量子叠加的问题对于格罗弗算法、量子神经网络和其他应用都很重要。本文提出的算法的目的是生成一个量子阵列,在n个量子位元上初始化所需的量子叠加。SQUID算法几乎总是创建比现有算法(如Ventura-Martinez和Long-Sun算法)创建的量子阵列性能更好的量子阵列。当叠加态包含所有可能的状态时,SQUID算法创建的量子阵列的最佳性能为O(n),这是所有现有算法的指数级改进。此外,SQUID算法创建的量子阵列的最坏情况性能不会比现有算法的性能差。SQUID算法比以前的量子叠加初始化算法有了很大的改进,并且可以比其他算法更有效地初始化量子叠加。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Superposed Quantum State Initialization Using Disjoint Prime Implicants (SQUID)
The problem of initializing a quantum superposition is important for Grover's algorithm, quantum neural networks and other applications. The purpose of the algorithm presented here is to generate a quantum array that initializes a desired quantum superposition on n qubits. The SQUID algorithm almost always creates quantum arrays that perform better than those created by existing algorithms such as the Ventura-Martinez and Long-Sun algorithms. The best case performance for the quantum arrays created by the SQUID algorithm is O(n) when the superposition contains all possible states which is an exponential improvement over all existing algorithms. Also, the worst case performance of the quantum array created by the SQUID algorithm is never worse than the performance of existing algorithms. The SQUID algorithm represents a vast improvement over previous quantum superposition initialization algorithms and allows quantum superpositions to be initialized much more efficiently than with other algorithms.
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