Machine learning assisted quantum photonics

Zhaxylyk A. Kudyshev; Alexander Kildishev; Alexandra Boltasseva; Vladimir M. Shalaev

doi:10.1117/12.2594736

2 August 2021 Machine learning assisted quantum photonics

Zhaxylyk A. Kudyshev, Alexander Kildishev, Alexandra Boltasseva, Vladimir M. Shalaev

Proceedings Volume 11797, Plasmonics: Design, Materials, Fabrication, Characterization, and Applications XIX; 1179706 (2021) https://doi.org/10.1117/12.2594736
Event: SPIE Nanoscience + Engineering, 2021, San Diego, California, United States

Abstract

In this talk we will highlight our most recent developments in 1) deep learning assisted optimization of photonic meta-structures and 2) machine learning-based algorithms for quantum photonic applications. We will discuss our studies on implementing deep-learning assisted topology optimization for advanced metasurface design development. We will also outline our recent work on merging topology optimization techniques with quantum device design development for achieving efficient on-chip integration. Finally, we will discuss approaches for implementing a novel convolutional neural network-based technique for real-time material defect metrology and quantum super-resolution microscopy applications.

Conference Presentation

Citation Download Citation

Zhaxylyk A. Kudyshev, Alexander Kildishev, Alexandra Boltasseva, and Vladimir M. Shalaev "Machine learning assisted quantum photonics", Proc. SPIE 11797, Plasmonics: Design, Materials, Fabrication, Characterization, and Applications XIX, 1179706 (2 August 2021); https://doi.org/10.1117/12.2594736

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