Accelerating De Novo Genome Assembly via Quantum-Assisted Graph Optimization with Bitstring Recovery
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Quantum Physics arXiv:2602.00156 (quant-ph) [Submitted on 29 Jan 2026] Title:Accelerating De Novo Genome Assembly via Quantum-Assisted Graph Optimization with Bitstring Recovery Authors:Jaya Vasavi Pamidimukkala, Himanshu Sahu, Ashwini Kannan, Janani Ananthanarayanan, Kalyan Dasgupta, Sanjib Senapati View a PDF of the paper titled Accelerating De Novo Genome Assembly via Quantum-Assisted Graph Optimization with Bitstring Recovery, by Jaya Vasavi Pamidimukkala and 4 other authors View PDF HTML (experimental) Abstract:Genome sequencing is essential to decode genetic information, identify organisms, understand diseases and advance personalized medicine. A critical step in any genome sequencing technique is genome assembly. However, de novo genome assembly, which involves constructing an entire genome sequence from scratch without a reference genome, presents significant challenges due to its high computational complexity, affecting both time and accuracy. In this study, we propose a hybrid approach utilizing a quantum computing-based optimization algorithm integrated with classical pre-processing to expedite the genome assembly process. Specifically, we present a method to solve the Hamiltonian and Eulerian paths within the genome assembly graph using gate-based quantum computing through a Higher-Order Binary Optimization (HOBO) formulation with the Variational Quantum Eigensolver algorithm (VQE), in addition to a novel bitstring recovery mechanism to improve optimizer traversal of the solution space. A comparative analysis with classical optimization techniques was performed to assess the effectiveness of our quantum-based approach in genome assembly. The results indicate that, as quantum hardware continues to evolve and noise levels diminish, our formulation holds a significant potential to accelerate genome sequencing by offering faster and more accurate solutions to the complex challenges in genomic research. Subjects: Quantum Physics (quant-ph); Genomics (q-bio.GN) Cite as: arXiv:2602.00156 [quant-ph] (or arXiv:2602.00156v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2602.00156 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Jaya Vasavi Pamidimukkala [view email] [v1] Thu, 29 Jan 2026 19:03:55 UTC (2,919 KB) Full-text links: Access Paper: View a PDF of the paper titled Accelerating De Novo Genome Assembly via Quantum-Assisted Graph Optimization with Bitstring Recovery, by Jaya Vasavi Pamidimukkala and 4 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph new | recent | 2026-02 Change to browse by: q-bio q-bio.GN 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?)