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机器学习与知识约束,用于优化微环共振器作为量子光源的设计优化
Parisa Sadeghli Dizaji1, Hamidreza Habibiyan2
1Departemant of Physics and Energy Engineering, Amirkabir University of Technology, Tehran, Iran.
Scientific reports
|January 3, 2025
概括
我们使用贝叶斯优化开发了一种机器学习框架,用于设计用于产生非经典光的微光振器. 这种方法显著加快了量子信息处理应用程序的设计过程.
科学领域:
- 综合光子学 综合光子学
- 量子信息科学是一种量子信息科学.
- 机器学习应用程序 机器学习应用程序
背景情况:
- 微环共振器是量子信息处理中紧的非经典光源的关键.
- 由于复杂,高维的参数空间,当前的设计方法面临着计算挑战.
研究的目的:
- 提出一个知识集成的机器学习框架,用于设计使用微环共振器的压缩光源.
- 为了克服集成光子设备的设计流程中的计算瓶.
主要方法:
- 利用贝叶斯优化 (BO) 进行自适应设计探索.
- 开发了一个针对微环共振器量身定制的知识集成机器学习框架.
- 专注于优化逃跑效率和芯片上挤压水平.
主要成果:
- 在5个优化回合内确定了两个最佳的微环共振器结构.
- 实现了超过90%的逃逸效率和7.48dB和9.86dB的芯片上挤压水平.
- 证明了BO在过度结合的地区寻找最佳设计的有效性.
结论:
- 开发的框架简化了基于微波振器的压缩光源的设计.
- 该方法适用于化共振器,并可扩展到其他材料和结构.
- 这种方法有助于设计用于量子电路和光学神经网络的各种集成光子组件.
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