通过接近最佳的量子过程断层扫描检索空间依赖的极化转换
Optics express
|October 20, 2023
概括
研究人员探索了量子过程断层扫描的遗传和机器学习方法. 这些技术准确地重建光学偏振转换,提供更快的操作,特别是实时应用.
科学领域:
- 量子光学就是一个量子光学.
- 量子信息科学是一种量子信息科学.
- 地元表面光学学
背景情况:
- 光学波形板可以被建模为单量子位单元运算符.
- 量子过程断层扫描 (QPT) 从测量中重建量子运算.
- 最大概率估计是一种标准的QPT方法,但可能是计算密集的.
研究的目的:
- 研究遗传学和机器学习算法的应用,用于光学过程断层扫描.
- 将这些新方法的性能与标准技术进行比较.
- 用旋转轨道元表面来实验性地描述空间依赖的极化转换.
主要方法:
- 模拟光波板作为单量子位单元运算符.
- 利用遗传算法和神经网络进行断层重建.
- 实验实施使用旋转轨道元表面与有图案的双折射.
- 将重建准确度和速度与最大概率估计进行比较.
主要成果:
- 遗传学和机器学习方法都能够准确地重建极化转换.
- 这些方法表明运行时间很快,特别是在最小的投射测量时.
- 基于神经网络的方案为实时表征提供了显著的加速.
- 实现了空间依赖的极化转换的成功实验性表征.
结论:
- 基因和机器学习方法为光学过程断层扫描提供了高效和准确的替代方案.
- 开发的技术扩展了用于描述光学和量子过程的现有方法.
- 这些发现为在更复杂的量子系统中优化断层扫描方法铺平了道路.
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