Machine-learning-assisted material and geometry characterization from Casimir force measurement

Quantum Physics [Submitted on 17 Apr 2026] Machine-learning-assisted material and geometry characterization from Casimir force measurement Hideo Iizuka, Shanhui Fan A broadband electromagnetic source is important for scientific and technological applications. Quantum vacuum fluctuations, which manifest most prominently in the Casimir effect, provide a fundamentally broadband electromagnetic source. Here we explore a potential consequence of the broadband nature of quantum vacuum fluctuations, by showing that such fluctuations can enable measurement of material permittivity over a broad frequency range. Specifically, we consider the Casimir force in a parallel-plate geometry, with one plate covered by a nanoscopic thin film. Using a machine learning approach, we show that one can infer both the thickness of the film and its permittivity over a broad frequency range, starting from the dependency of the Casimir forces on the spacing between the two plates. Our work highlights the application potential of using vacuum fluctuations as a naturally-existing broadband electromagnetic source for material characterization, and shows that the inverse problem in Casimir force calculation can be solved with machine learning. Subjects: Quantum Physics (quant-ph); Optics (physics.optics) Cite as: arXiv:2604.15763 [quant-ph]   (or arXiv:2604.15763v1 [quant-ph] for this version)   https://doi.org/10.48550/arXiv.2604.15763 Focus to learn more Submission history From: Hideo Iizuka [view email] [v1] Fri, 17 Apr 2026 07:07:20 UTC (2,489 KB) Access Paper: view license Current browse context: quant-ph < prev   |   next > new | recent | 2026-04 Change to browse by: physics physics.optics References & Citations INSPIRE HEP NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)