Molecular Docking with Quantum Circuit Evolution

Quantum Physics arXiv:2607.12060 (quant-ph) [Submitted on 13 Jul 2026] Title:Molecular Docking with Quantum Circuit Evolution Authors:Gleydson Fernandes de Jesus, Bruno Oziel Fernandez, Marcelo A. Moret View a PDF of the paper titled Molecular Docking with Quantum Circuit Evolution, by Gleydson Fernandes de Jesus and 1 other authors View PDF HTML (experimental) Abstract:Molecular docking is an important step in drug discovery, enabling the evaluation of receptor-ligand affinity while reducing experimental costs and increasing the number of possible tests. However, the high computational cost associated with molecular docking remains a limiting factor that can restrict both the experimental precision and the scale of the problems being addressed. To improve the future applicability of molecular docking, recent works have proposed the use of quantum algorithms based on Gaussian Boson Sampling quantum computers and also gate-based quantum computers. In this work, we propose the use of Quantum Circuit Evolution (QCE) for solving the molecular docking problem, a gate based and gradient-free quantum evolutionary method whose evolution is driven by the random application of unitary operations to a quantum circuit. The proposed algorithm demonstrated the ability to find the best solution to the problem in fewer steps than the methods presented in previous studies, exhibiting fast and stable convergence. Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2607.12060 [quant-ph] (or arXiv:2607.12060v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2607.12060 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Gleydson Fernandes De Jesus [view email] [v1] Mon, 13 Jul 2026 18:25:48 UTC (1,361 KB) Full-text links: Access Paper: View a PDF of the paper titled Molecular Docking with Quantum Circuit Evolution, by Gleydson Fernandes de Jesus and 1 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph new | recent | 2026-07 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?)