Exploring the use of quantum computing for facilitating spatially and temporally resolved models of a biological cell

Quantum Physics arXiv:2607.27571 (quant-ph) [Submitted on 30 Jul 2026] Title:Exploring the use of quantum computing for facilitating spatially and temporally resolved models of a biological cell Authors:Muralikrishnan Gopalakrishnan Meena, Dileep Kishore, Jerry M. Parks, Luke Bertels, Dilipkumar N. Asthagiri, Travis Humble, Thomas L. Beck, Mitchel J. Doktycz View a PDF of the paper titled Exploring the use of quantum computing for facilitating spatially and temporally resolved models of a biological cell, by Muralikrishnan Gopalakrishnan Meena and 7 other authors View PDF HTML (experimental) Abstract:Whole-cell simulation, modeling all of a cell's functional systems over its life cycle, is an outstanding challenge in computational biology. Even the simplest living cell contains thousands of interacting proteins and metabolites (on the order of trillions of atoms) whose full functional dynamics spans roughly five orders of magnitude in space (nm to $\mu$m) and nearly nineteen in time (fs to hours). Further, many of the governing physical and chemical properties remain incompletely characterized. Simulating such complex systems at fully atomistic resolution over a full cell cycle is computationally intractable on classical architectures, raising a central question: Can quantum computing offer a viable path to whole-cell simulations that integrate molecular- and systems-level complexity? This Perspective examines the potential of quantum computing across three hierarchical scales: atomistic-molecular modeling, metabolic and regulatory networks, and whole-cell spatial modeling. We present a complexity analysis comparing classical and quantum algorithms for representative biological problems, identifying regimes of substantial theoretical speedup under specified algorithmic assumptions. We highlight algorithmic developments designed to leverage both near-term exploratory and fault-tolerant quantum architectures, and discuss practical bottlenecks: data encoding overhead, system conditioning, measurement constraints, and hybrid quantum-HPC integration. Together, these results outline a roadmap for quantum-accelerated whole-cell modeling and the biological insights such multiscale frameworks may eventually enable. Subjects: Quantum Physics (quant-ph); Biological Physics (physics.bio-ph) Cite as: arXiv:2607.27571 [quant-ph] (or arXiv:2607.27571v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2607.27571 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Muralikrishnan Gopalakrishnan Meena [view email] [v1] Thu, 30 Jul 2026 01:24:19 UTC (976 KB) Full-text links: Access Paper: View a PDF of the paper titled Exploring the use of quantum computing for facilitating spatially and temporally resolved models of a biological cell, by Muralikrishnan Gopalakrishnan Meena and 7 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph new | recent | 2026-07 Change to browse by: physics physics.bio-ph 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?) 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?)