通过自适应性遗传算法准备旋转挤压状态
Y M Zhao1, L B Chen1, W Z Zhang2
1Qingdao University of Technology, School of Physics, Qingdao 0532, Shandong, China.
Physical review. E
|January 21, 2026
概括
我们开发了一种自适应基因算法 (GA),以优化量子控制序列,以创建非经典状态. 这种方法有效地准备了自旋挤压状态,在杂的量子系统中表现优于其他技术.
科学领域:
- 量子控制是一种量子控制.
- 量子信息科学 量子信息科学
- 量子计量学 量子计量学
背景情况:
- 产生非经典状态对于量子技术至关重要.
- 在现实的,杂的环境中控制量子系统面临重大挑战.
研究的目的:
- 引入和验证一种适应性遗传算法 (GA),用于优化量子控制序列.
- 用GA来证明在一个开放的量子系统中准备自旋压缩状态的过程.
- 评估GA的性能与其他控制策略相比.
主要方法:
- 使用适应性遗传算法 (GA) 与进化策略 (交叉,突变,消除).
- 控制序列的代优化,从一个连贯的旋转状态开始.
- 模拟一个开放的集体旋转模型与消散和脱相.
- 与不断控制和强化学习方法进行基准测试.
主要成果:
- 对于旋转挤压状态,实现了高状态准备保真度 (>0.99).
- 证明了强大的和可扩展的性能,即使在消散和热噪声.
- 展示了一个长时间窗口来保持旋转挤压状态.
- 对其他方法来说,GA的表现具有竞争力,并且强大.
结论:
- 适应式GA是一个强大而通用的工具,用于生成非经典的量子状态.
- 该策略可用于优化噪音系统中的计量学相关挤压.
- 这项工作为在量子系统控制中使用类似GA的策略提供了基础.
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