Inverse design in quantum nanophotonics: combining local-density-of-states and deep learning
Author:
Liu Guang-Xin1, Liu Jing-Feng1, Zhou Wen-Jie2, Li Ling-Yan1, You Chun-Lian1, Qiu Cheng-Wei3, Wu Lin24ORCID
Affiliation:
1. College of Electronic Engineering and College of Artificial Intelligence , South China Agricultural University , Guangzhou 510642 , China 2. Science, Mathematics and Technology (SMT) , Singapore University of Technology and Design (SUTD) , 8 Somapah Road , Singapore 487372 , Singapore 3. Department of Electrical and Computer Engineering , National University of Singapore , 4 Engineering Drive 3 , Singapore 117583 , Singapore 4. Agency for Science, Technology, and Research (A*STAR) , Institute of High Performance Computing , Singapore 138632 , Singapore
Abstract
Abstract
Recent advances in inverse-design approaches for discovering optical structures based on desired functional characteristics have reshaped the landscape of nanophotonic structures, where most studies have focused on how light interacts with nanophotonic structures only. When quantum emitters (QEs), such as atoms, molecules, and quantum dots, are introduced to couple to the nanophotonic structures, the light–matter interactions become much more complicated, forming a rapidly developing field – quantum nanophotonics. Typical quantum functional characteristics depend on the intrinsic properties of the QE and its electromagnetic environment created by the nanophotonic structures, commonly represented by a scalar quantity, local-density-of-states (LDOS). In this work, we introduce a generalized inverse-design framework in quantum nanophotonics by taking LDOS as the bridge to connect the nanophotonic structures and the quantum functional characteristics. We take a simple system consisting of QEs sitting on a single multilayer shell–metal–nanoparticle (SMNP) as an example, apply fully-connected neural networks to model the LDOS of SMNP, inversely design and optimize the geometry of the SMNP based on LDOS, and realize desirable quantum characteristics in two quantum nanophotonic problems: spontaneous emission and entanglement. Our work introduces deep learning to the quantum optics domain for advancing quantum device designs; and provides a new platform for practicing deep learning to design nanophotonic structures for complex problems without a direct link between structures and functional characteristics.
Funder
China Scholarship Council National Natural Science Foundation of China National Research Foundation Singapore Singapore University of Technology and Design
Publisher
Walter de Gruyter GmbH
Subject
Electrical and Electronic Engineering,Atomic and Molecular Physics, and Optics,Electronic, Optical and Magnetic Materials,Biotechnology
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