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.

中文翻译：

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量子纳米光子学中的逆向设计：结合局部态密度和深度学习

基于所需功能特性发现光学结构的逆向设计方法的最新进展重塑了纳米光子结构的景观，其中大多数研究只关注光如何与纳米光子结构相互作用。当原子、分子和量子点等量子发射体 (QE) 被引入耦合到纳米光子结构时，光与物质的相互作用变得更加复杂，形成了一个快速发展的领域——量子纳米光子学。典型的量子功能特性取决于 QE 的固有特性及其由纳米光子结构创建的电磁环境，通常由标量、局域态密度 (LDOS) 表示。在这项工作中，我们通过以 LDOS 作为连接纳米光子结构和量子功能特性的桥梁，在量子纳米光子学中引入了一个广义逆向设计框架。我们以一个由位于单个多层壳-金属-纳米粒子（SMNP）上的 QE 组成的简单系统为例，应用全连接神经网络对 SMNP 的 LDOS 进行建模，基于 LDOS 逆向设计和优化 SMNP 的几何结构，并在两个量子纳米光子问题中实现理想的量子特性：自发发射和纠缠。我们的工作将深度学习引入量子光学领域，以推进量子器件设计；并为实践深度学习提供了一个新平台，可以在结构和功能特性之间没有直接联系的情况下为复杂问题设计纳米光子结构。