ArticleNanophotonics (Berlin, Germany)2023
Inverse design in quantum nanophotonics: combining local-density-of-states and deep learning.
Article in Nanophotonics (Berlin, Germany), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- MxDiffusion: A Physics-Aware Maxwell's Law-Guided Diffusion Model Strategy for Inverse Photonic Metasurface Design.Nano letters · 2026Article
- Deep learning methods for 2D material electronic properties.Digital discovery · 2026Review
- Recent advances in the inverse design of silicon photonic devices and related platforms using deep generative models.PeerJ. Computer science · 2025Article
- Identifying topology of leaky photonic lattices with machine learning.Nanophotonics (Berlin, Germany) · 2024Article
- Designing Metasurfaces for Efficient Solar Energy Conversion.ACS photonics · 2023Review
- Machine Learning and Artificial Intelligence in Nanomedicine.Wiley interdisciplinary reviews. Nanomedicine and nanobiotechnologyReview
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Authors and funding
7 authors.
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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.
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