Quantum Computing and Error Mitigation with Deep Learning for Frenkel Excitons

Quantum Physics [Submitted on 25 Mar 2026] Quantum Computing and Error Mitigation with Deep Learning for Frenkel Excitons Yi-Ting Lee, Vijaya Begum-Hudde, Barbara A. Jones, André Schleife 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 Submission history From: Yi-Ting Lee [view email] [v1] Wed, 25 Mar 2026 04:57:04 UTC (2,696 KB) Access Paper: view license Current browse context: quant-ph < prev   |   next > new | recent | 2026-03 Change to browse by: cond-mat cond-mat.mtrl-sci 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?)