发表机构
Ochanomizu University（御茶水女子大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出将基于反馈的量子优化算法（FALQON）与XY混频器结合用于图着色问题，实验表明其性能优于使用X混频器的方案。
AI 中文摘要
基于反馈的量子优化算法（FALQON）是量子近似优化算法（QAOA）的一种扩展，为寻找组合优化问题的近似解提供了一种有前景的方法。FALQON通过利用迭代测量得到的信息，避免了变分参数的优化。对于受约束的组合优化问题，使用XY混频器有时比传统的X混频器更有效。我们将带有XY混频器的FALQON应用于图着色问题，并研究了问题哈密顿量的期望值、图着色的成功概率以及若干小规模问题的公平采样。带有XY混频器的FALQON表现出优于带有X混频器的性能。
英文摘要
The feedback-based algorithm for quantum optimization (FALQON), which is an extension of the quantum approximate optimization algorithm (QAOA), is a promising approach for finding approximate solutions to combinatorial optimization problems. The FALQON avoids the optimization of variational parameters by using information derived from iterative measurements. To solve a constrained combinatorial optimization problem, it is sometimes more efficient to use an XY mixer rather than the conventional X mixer. We applied the FALQON with the XY mixer to graph coloring and investigated the expectation value of the problem Hamiltonian, success probability of graph coloring, and fair sampling for several small-scale problems. The FALQON with the XY mixer exhibited better performance than that with the X mixer.
Comments13 pages, 5 figures
