标题： 基于物理信息量子GAN的逆设计用于介电超表面的定制吸收 显示英文标题

标题： Inverse Design using Physics-Informed Quantum GANs for Tailored Absorption in Dielectric Metasurfaces

摘要： 高Q因子的窄带吸收表现出高的光谱选择性，使得高灵敏度的光电探测器、传感器和热辐射器成为可能。全介质超表面被广泛认为是产生这种窄带吸收的优秀候选者。然而，设计具有特定功能的超表面在实验和计算上仍然是一个具有挑战性的任务，这就是为什么越来越多地探索逆向设计方法。由于逆向设计过程存在非唯一解和设计空间的高维性，因此非常复杂，使得精确控制共振波长、线宽和吸收强度变得困难。在本文中，我们提出了一种新的混合方法，将生成对抗网络（GANs）（包括经典和量子）与物理信息神经网络（PINNs）结合起来，用于窄带吸收超表面的逆向设计。通过将Fano形状的吸收光谱方程引入PINN损失函数，我们对共振行为施加了物理约束，确保输出既在光谱上准确又在物理上一致。本研究比较了传统GAN + PINN框架与由混合量子-经典GAN（QGAN）增强的PINN。研究结果表明，集成的PINN+QGAN模型收敛速度更快，所需的训练样本减少了99.5%，并且与传统GAN相比，均方误差降低了数量级。值得注意的是，尽管训练数据集仅包含Q因子在$10^3$量级的超表面，该模型仍能够生成Q因子超过$10^5$的高不对称超表面结构。本研究提出了一种将量子机器学习与基于物理的建模相结合的新框架，为纳米光子系统中的量子增强逆向设计提供了一种有前景的方法。 显示英文摘要

摘要： High Q-factor narrow-band absorption exhibits high spectral selectivity enabling high-sensitive photodetectors, sensors and thermal emitters. All-dielectric metasurfaces are widely regarded as excellent candidates for giving rise to such narrow-band absorption. However, designing metasurfaces with specific functionalities remains a challenging task both experimentally and computationally, which is why inverse design methods are increasingly being explored. Inverse design process is highly complex due to its non-unique solutions and the higher dimensionality of the design space, making it challenging to precisely control the resonance wavelength, linewidth, and absorption intensity. In this paper, we present a novel hybrid methodology that integrates generative adversarial networks (GANs) (both classical and quantum) with physics-informed neural networks (PINNs) for the inverse design of narrow-band absorbing metasurfaces. By introducing a Fano-shaped absorption spectrum equation into the PINN loss function, we enforce physical constraints on the resonance behavior, ensuring outputs that are both spectrally accurate and physically consistent. The study presents a comparison between a conventional GAN + PINN framework and a PINN augmented by a hybrid quantum-classical GAN (QGAN). The findings indicate that the integrated PINN + QGAN model achieves faster convergence, requires 99.5\% fewer training samples, and yields an order of magnitude lower MSE compared to conventional GANs. Remarkably, even though the training dataset only contains metasurfaces with Q-factors on the order of $10^3$, the model is able to generate highly asymmetric metasurface structures with Q-factors exceeding $10^5$. This study presents a novel framework that integrates quantum machine learning with physics-based modeling, providing a promising method for quantum-enhanced inverse design in nanophotonic systems.