发表机构
Universidade Federal de Santa Catarina（圣卡塔琳娜联邦大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文统一了时间重标度与二阶FALQON变体，降低了电路深度并允许灵活选择时间步长，同时保持稳定解，适用于NISQ时代。
AI 中文摘要
基于反馈的量子算法近期因其展示了如何在不借助混合量子-经典架构的情况下使用量子计算机解决优化问题而受到关注。近期在量子优化反馈算法（FALQON）变体方面取得的进展显著降低了所需电路的深度。在这项工作中，我们将时间重标度和二阶FALQON变体合并到一个统一框架中。结果表明，与过去的修改相比，这一方法取得了重要改进，降低了电路深度，并允许更灵活地选择时间步长，同时保持稳定的解。这种FALQON变体使其适用于NISQ时代。
英文摘要
Feedback-based quantum algorithms recently gained attention by demonstrating how quantum computers can be used to solve optimization problems without resorting to hybrid quantum-classical architectures. Recent progress made with Feedback-based Algorithm for Quantum Optimization (FALQON) variants significantly reduced the depth of the required circuits. In this work, we merge the time-rescaled and second-order FALQON variants into a unified framework. The results show an important improvement over past modifications, reducing circuit depth and allowing a more flexible choice of time-steps, while maintaining stable solutions. This FALQON variant makes it suitable for the NISQ era.
Comments7 pages, 4 figures, 2 tables
