Quantum Computing and Error Mitigation with Deep Learning for Frenkel Excitons
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Quantum Physics arXiv:2603.23936 (quant-ph) [Submitted on 25 Mar 2026] Title:Quantum Computing and Error Mitigation with Deep Learning for Frenkel Excitons Authors:Yi-Ting Lee, Vijaya Begum-Hudde, Barbara A. Jones, André Schleife View a PDF of the paper titled Quantum Computing and Error Mitigation with Deep Learning for Frenkel Excitons, by Yi-Ting Lee and 3 other authors View PDF Abstract:Quantum computers, currently in the noisy intermediate-scale quantum (NISQ) era, have started to provide scientists with a novel tool to explore quantum physics and chemistry. While several electronic systems have been extensively studied, Frenkel excitons, as prototypical optical excitations, remain among the less-explored applications. Here, we first use variational quantum deflation to calculate the eigenstates of the Frenkel Hamiltonian and evaluate the observables based on the oscillator strength for each eigenstate. Furthermore, using NISQ quantum computers requires performing error mitigation techniques alongside simulations. To deal with noisy qubits, we developed a deep-learning-based framework combined with a post-selection technique to learn the noise pattern and mitigate the error. Our mitigation methods work well and outperform the conventional post-selection and remain valid on real hardware. Subjects: Quantum Physics (quant-ph); Materials Science (cond-mat.mtrl-sci) Cite as: arXiv:2603.23936 [quant-ph] (or arXiv:2603.23936v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2603.23936 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Yi-Ting Lee [view email] [v1] Wed, 25 Mar 2026 04:57:04 UTC (2,696 KB) Full-text links: Access Paper: View a PDF of the paper titled Quantum Computing and Error Mitigation with Deep Learning for Frenkel Excitons, by Yi-Ting Lee and 3 other authorsView PDFTeX Source view license Current browse context: quant-ph new | recent | 2026-03 Change to browse by: cond-mat cond-mat.mtrl-sci References & Citations INSPIRE HEP NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: 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 Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) Links to Code Toggle Papers with Code (What is Papers with Code?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)