Efficient Simulation of Pre-Born-Oppenheimer Dynamics on a Quantum Computer
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Quantum Physics arXiv:2602.11272 (quant-ph) [Submitted on 11 Feb 2026] Title:Efficient Simulation of Pre-Born-Oppenheimer Dynamics on a Quantum Computer Authors:Matthew Pocrnic, Ignacio Loaiza, Juan Miguel Arrazola, Nathan Wiebe, Danial Motlagh View a PDF of the paper titled Efficient Simulation of Pre-Born-Oppenheimer Dynamics on a Quantum Computer, by Matthew Pocrnic and 4 other authors View PDF Abstract:In this work, we present a quantum algorithm for direct first-principles simulation of electron-nuclear dynamics on a first-quantized real-space grid. Our algorithm achieves best-in-class efficiency for block-encoding the pre-Born-Oppenheimer molecular Hamiltonian by harnessing the linear scaling of swap networks for implementing the quadratic number of particle interactions, while using a novel alternating sign implementation of the Coulomb interaction that exploits highly optimized arithmetic routines. We benchmark our approach for a series of scientifically and industrially relevant chemical reactions. We demonstrate over an order-of-magnitude reduction in costs compared to previous state-of-the-art for the $\rm NH_3+BF_3$ reaction, achieving a Toffoli cost of $8.7\times10^{9}$ per femtosecond using $1362$ logical qubits (system + ancillas). Our results significantly lower the resources required for fault-tolerant simulations of photochemical reactions, while providing a suite of algorithmic primitives that are expected to serve as foundational building blocks for a broader class of quantum algorithms. Comments: Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2602.11272 [quant-ph] (or arXiv:2602.11272v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2602.11272 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Ignacio Loaiza [view email] [v1] Wed, 11 Feb 2026 19:00:03 UTC (988 KB) Full-text links: Access Paper: View a PDF of the paper titled Efficient Simulation of Pre-Born-Oppenheimer Dynamics on a Quantum Computer, by Matthew Pocrnic and 4 other authorsView PDFTeX Source view license Current browse context: quant-ph new | recent | 2026-02 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?)