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arXiv 2202.02774quant-ph
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利用绝热量子计算在大型互连图与网络上的优化

Optimization on Large Interconnected Graphs and Networks Using Adiabatic Quantum Computation

Venkat Padmasola, Rupak Chatterjee

AI总结:

本研究提出一种最多用3V个量子比特求解无向图最短路径的绝热量子计算算法,无需经典算法参与(邻接矩阵构建除外),验证了绝热量子计算机结合随机图生成器建模大型图的可行性。

摘要:

本文证明,我们可构建一种绝热量子计算算法,用于求解无向图上任意两顶点间的最短路径,该算法最多仅需3V个量子比特,其中V为图的顶点数。除构建(V×V)邻接矩阵的步骤外,整个求解过程不依赖任何经典算法。本文的研究目标是证明如下事实:我们可在绝热量子计算机上对大型图进行建模,过程中可利用可用的最大量子比特数,并借助Barabasi-Albert(巴拉巴西-阿尔伯特)和Erdos-Renyi(埃尔德什-雷尼)这类可遵循幂律扩展规模的随机图生成器实现大图建模。

英文摘要:

In this paper, we demonstrate that it is possible to create an adiabatic quantum computing algorithm that solves the shortest path between any two vertices on an undirected graph with at most 3V qubits, where V is the number of vertices of the graph. We do so without relying on any classical algorithms, aside from creating a (V x V) adjacency matrix. The objective of this paper is to demonstrate the fact that it is possible to model large graphs on an adiabatic quantum computer using the maximum number of qubits available and random graph generators such as the Barabasi-Albert and the Erdos-Renyi methods which can scale based on a power law.

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。

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