Displaying items by tag: quantum optics
This blog post is based on the publication P.-I. Schneider, et al. Benchmarking five global optimization approaches for nano-optical shape optimization and parameter reconstruction.ACS Photonics 6, 2726 (2019). Several global optimization methods for three typical nano-optical optimization problems are benchmarked: particle swarm optimization, differential evolution, and Bayesian optimization as well as multistart versions of downhill simplex optimization and the limited-memory Broydenu2013Fletcheru2013Goldfarbu2013Shanno (L-BFGS) algorithm. In the shown examples, Bayesian optimization, mainly known from machine learning applications, obtains significantly better results in a fraction of the run times of the other optimization methods.
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This blog post is based on the publication P.-I. Schneider, et al. Numerical optimization of the extraction efficiency of a quantum-dot based single-photon emitter into a single-mode fiber. Opt. Express 26, 8479 (2018). The publication introduces a finite-element method for the accurate and efficient simulation of strongly localized light sources, such as quantum dots, embedded in dielectric micro-optical structures. The method is applied in order to optimize the photon extraction efficiency of a single-photon emitter and to study the robustness of the extraction efficiency with respect to fabrication errors and defects.
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