标题： 超材料的混合量子-经典逆向设计用于定制窄带吸收 显示英文标题

标题： Hybrid Quantum-Classical Inverse Design of Metasurfaces for Tailored Narrow Band Absorption

摘要： 超表面的逆向设计由于结构特性与电磁响应之间的复杂相互依赖关系而面临重大挑战。 传统优化方法需要大量的计算资源，并且经常无法产生最有效的解决方案。 本研究提出了一种混合量子-经典机器学习方法，称为潜在风格量子生成对抗网络（LaSt-QGAN）。 该方法将变分自编码器（VAE）与量子生成对抗网络（QGAN）相结合，以提高旨在实现窄带吸收和单向性的超表面设计的优化。 与传统的基于GAN的方法相比，该方法使训练时间减少了10倍，数据需求减少了40倍。 与基于经典GAN的方法相比，所生成的超表面设计在目标吸收光谱方面表现出高保真度。 此外，材料查找表的集成通过允许将预测的材料特性替换为可行的替代品，同时保持性能准确性，从而促进了可制造性。 此外，该模型能够生成高达$10^4$的Q因子，而训练数据集的Q因子最高可达$10^3$。 显示英文摘要

摘要： The inverse design of metasurfaces poses a considerable challenge because of the intricate interdependencies that exist between structural characteristics and electromagnetic responses. Traditional optimization methods require significant computational resources and frequently do not produce the most effective solutions. This study presents a hybrid quantum-classical machine learning approach known as Latent Style-based Quantum GAN (LaSt-QGAN). This method integrates a Variational Autoencoder (VAE) with a Quantum Generative Adversarial Network (QGAN) to enhance the optimization of metasurface designs aimed at achieving narrow-band absorption and unidirectionality. The proposed method results in a reduction of training time by 10X and a decrease in data requirements by 40X when compared to traditional GAN-based approaches. The produced metasurface designs demonstrate a high fidelity in relation to the target absorption spectra compared to the classical GAN based methods. Additionally, the integration of a material look-up table facilitates manufacturability by allowing for the substitution of predicted material properties with viable alternatives, all while preserving performance accuracy. Moreover the model is able to generate Q-factor upto the order of $10^4$, while the training dataset has Q-factor upto the order of $10^3$.