通过离散实时学习和最佳控制理论的协同作用来控制多体量子系统的模拟
Shaojun Gui1, Tak-San Ho1, Herschel Rabitz1
1Department of Chemistry, Princeton University, Princeton, New Jersey 08544, USA.
The Journal of chemical physics
|September 8, 2025
概括
我们开发了一种结合机器学习和量子控制理论的自我一致的算法,用于模拟多体量子系统. 这种方法有效地识别了关键的量子状态,使得使用更少的计算资源实现了精确的控制.
科学领域:
- 量子物理学 量子物理学 是一种量子物理学.
- 计算科学 计算科学
- 机器学习 机器学习
背景情况:
- 模拟复杂的多体量子系统需要大量的计算资源.
- 量子动力学的最佳控制对于量子技术至关重要,但在计算上要求很高.
- 现有的方法在多体系统中与希尔伯特空间的高维度作斗争.
研究的目的:
- 介绍一种新的自我一致的算法,用于对多体量子系统的高效最佳控制模拟.
- 将离散实时机器学习 (DRTL) 与量子最佳控制理论 (QOCT) 协同结合起来.
- 为了降低与模拟和控制量子系统相关的计算成本.
主要方法:
- 引入了整合DRTL和QOCT的两步协同算法.
- 在系统动态的希尔伯特空间中,DRTL确定了一个紧的"工作空间".
- QOCT使用这个工作空间来代地改进控制字段,直到实现一个自相一致的目标.
主要成果:
- 该算法成功地在1D和2D海森堡旋转系统上进行了最佳控制模拟.
- 它有效地确定了相关的工作空间,并在少数代中实现了受控动态的融合.
- 工作空间的维度与旋转数量成准线性缩放,证明了可扩展性.
结论:
- 协同DRTL-QOCT算法为量子最佳控制提供了一个计算效率高的方法.
- 它通过专注于基本动力学,有效地管理多体量子系统的复杂性.
- 这种方法对推进量子模拟和控制应用有前途.
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