Inverse Design using Physics-Informed Quantum GANs for Tailored Absorption in Dielectric Metasurfaces
Fuente:
arXiv
Guardado en:
| Autores principales: | , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866912499497959424 |
|---|---|
| author | Warrier, Sreeraj Rajan Dontabhaktuni, Jayasri |
| author_facet | Warrier, Sreeraj Rajan Dontabhaktuni, Jayasri |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_18132 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Inverse Design using Physics-Informed Quantum GANs for Tailored Absorption in Dielectric Metasurfaces Warrier, Sreeraj Rajan Dontabhaktuni, Jayasri Optics Soft Condensed Matter 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. |
| title | Inverse Design using Physics-Informed Quantum GANs for Tailored Absorption in Dielectric Metasurfaces |
| topic | Optics Soft Condensed Matter |
| url | https://arxiv.org/abs/2507.18132 |