基于多目标深度强化学习的超导量子计算优化.
1School of Physics, Xi'an Jiaotong University, No.28 Xianning West Road, Xi'an, 710049, Shaanxi, P. R. China. 18954559536@stu.xjtu.edu.cn.
Scientific reports
|January 30, 2025
概括
我们开发了一种多目标的深度强化学习方法,用于量子最佳控制. 这种方法为复杂的量子系统找到全球最佳解决方案,优于本地优化.
科学领域:
- 量子计算是一种量子计算.
- 人工智能的人工智能是人工智能.
- 优化理论 优化理论
背景情况:
- 深度强化学习 (DRL) 是有效的量子优化和最佳控制.
- 精确的量子测量需要在多个实验阶段进行模拟控制.
- 目前的方法可能会产生局部最佳值,限制对复杂量子系统的控制.
研究的目的:
- 通过使用一种新的DRL方法来增强多过程量子最佳控制.
- 开发一种方法,在考虑多个因素的情况下实现全球最佳解决方案.
- 通过先进的模拟控制来提高量子测量的精度.
主要方法:
- 改进了一种基于数学凸优化的多目标深度强化学习 (DRL) 方法.
- 利用单个过程量子控制优化结果作为截断值和奖励函数转移策略.
- 应用了该方法来优化超导量子比特的微波脉冲参数.
主要成果:
- 实现了多过程量子最佳控制的全球最佳解决方案,超越了局部最佳.
- 在超导量子比特系统上展示了出色的计算结果.
- 提供了一组全球参数值和控制策略,以实现最佳控制.
结论:
- 改进的DRL方法有效地解决了多过程量子最佳控制的挑战.
- 这种方法可以精确控制复杂的量子系统,从而获得更好的测量结果.
- 这些发现为在量子计算应用中实现全球最佳控制提供了一条途径.
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