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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.
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Zhaxylyk A. Kudyshev, Alexander Kildishev, Alexandra Boltasseva, 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