通过算法量子化采样各种近乎最佳的解决方案
Masoud Mohseni1,2, Marek M Rams3, Sergei V Isakov4
1Google Quantum AI, Venice, California 90291, USA.
Physical review. E
|January 20, 2024
概括
研究人员开发了一种新的多样性测量方法,用于量化复杂优化问题的解决方案. 这个度量,时间对多样性 (TTD),增强了基准测试,并揭示了不均的量子化时间表的优势.
科学领域:
- 计算物理学的计算物理.
- 量子计算是一种量子计算.
- 运营研究 运营研究
背景情况:
- 艰难的优化问题需要多样化,高质量的解决方案.
- 随机求解器经常遭受模式崩,限制了强度.
- 缺乏一个统一的指标来量化解决者性能缺陷.
研究的目的:
- 引入一种新的多样性测量方法,用于NP-hard优化问题.
- 开发一个时间到多样性 (TTD) 度量用于基准测试解决者.
- 比较量子回火策略的采样能力.
主要方法:
- 为近似解决方案引入了新的多样性衡量标准.
- 定义时间到多样性 (TTD) 作为绩效基准.
- 使用路径积分蒙特卡罗模拟和张量网络收缩.
主要成果:
- 不均的量子化计划可以改善溶解时间 (TTS) 和 TTD.
- 量子波动的非平衡驱动可以提高多达40%的溶液多样性.
- 难以采样实例的比例减少了25%以上.
结论:
- 新的多样性指标和TTD指标提供了有效的基准测试.
- 有控制的拓缺陷的量子化提供了一个优势.
- 算法量子相位过渡增强了硬实例的解决方案多样性.
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