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arXiv 2305.08056quant-phcs.SYeess.SYmath.OCq-fin.PM
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混合量子算法:集成QAOA、惩罚退相与Zeno效应求解多约束二元优化问题

Hybrid Quantum Algorithms integrating QAOA, Penalty Dephasing and Zeno Effect for Solving Binary Optimization Problems with Multiple Constraints

Ke Wan, Yiwen Liu

AI总结:

本文提出一种混合量子算法框架,结合QAOA、惩罚退相和Zeno效应,以非Ising公式处理多约束,有效求解二元优化问题,并在飞机装载问题中展现优异性能。

摘要:

在使用量子算法处理二元优化问题时,传统的Ising表示和量子近似优化算法(QAOA)在高效处理涉及多约束的大规模问题中的错误方面遇到困难。为应对这些挑战,本文提出了一种混合框架,该框架结合使用标准Ising哈密顿量来求解一部分约束,同时采用非Ising公式来表示并处理其余约束。这些非Ising约束的求解通过惩罚退相或量子Zeno效应实现。这种创新方法产生了一系列具有自适应结构的量子电路,具体结构取决于每个约束所选的表示方式。此外,本文还引入了一种新技术,通过频繁测量约束标志来利用量子Zeno效应,从而能够求解任意优化约束。本文讨论了这些算法的理论性质,并在解决实际飞机装载问题中展现了极具前景的性能,显示出在广泛工业应用中的巨大潜力。

英文摘要:

When tackling binary optimization problems using quantum algorithms, the conventional Ising representation and Quantum Approximate Optimization Algorithm (QAOA) encounter difficulties in efficiently handling errors for large-scale problems involving multiple constraints. To address these challenges, this paper presents a hybrid framework that combines the use of standard Ising Hamiltonians to solve a subset of the constraints, while employing non-Ising formulations to represent and address the remaining constraints. The resolution of these non-Ising constraints is achieved through either penalty dephasing or the quantum Zeno effect. This innovative approach leads to a collection of quantum circuits with adaptable structures, depending on the chosen representation for each constraint. Furthermore, this paper introduces a novel technique that utilizes the quantum Zeno effect by frequently measuring the constraint flag, enabling the resolution of any optimization constraint. Theoretical properties of these algorithms are discussed, and their performance in addressing practical aircraft loading problems is highly promising, showcasing significant potential for a wide range of industrial applications.

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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