MAFFT-inspired Quantum Shift-based Sequence Alignment and its Efficient Simulation on Decision Diagrams
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Quantum Physics arXiv:2602.23848 (quant-ph) [Submitted on 27 Feb 2026] Title:MAFFT-inspired Quantum Shift-based Sequence Alignment and its Efficient Simulation on Decision Diagrams Authors:Yusuke Kimura, Yutaka Takita View a PDF of the paper titled MAFFT-inspired Quantum Shift-based Sequence Alignment and its Efficient Simulation on Decision Diagrams, by Yusuke Kimura and 1 other authors View PDF HTML (experimental) Abstract:Multiple sequence alignment (MSA) is a core operation for comparing genome sequences and is widely used in bio-informatics. MAFFT, a practical MSA tool, repeatedly shifts a pair of sequences and computes a distance. Because the number of sequence pairs grows quadratically with the number of sequences, this procedure can become a bottleneck. We propose Quantum Shift-based Sequence Alignment (QShift-SA), which implements this ``shift-wise score computation'' as a gate-based quantum circuit and searches over shift amounts and sequence pairs using Grover algorithm. QShift-SA constructs an oracle circuit that compute the Hamming distance (the number of mismatches) between two sequences with data encoding, controlled shift, comparison, and addition. This oracle can search for candidates with small distances. QShift-SA does not aim to replace the full MSA workflow; instead, it targets the screening steps that often dominate the runtime in classical MAFFT as stated above. We evaluate circuit resources (number of qubits, gate count, and depth) and benchmark simulation time across multiple quantum circuit simulators. We find that a decision diagram (DD)-based quantum circuit simulator runs more than 1,000$\times$ faster than state-vector and MPS simulators and can handle larger circuits. Comments: Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2602.23848 [quant-ph] (or arXiv:2602.23848v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2602.23848 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Yusuke Kimura [view email] [v1] Fri, 27 Feb 2026 09:42:57 UTC (1,244 KB) Full-text links: Access Paper: View a PDF of the paper titled MAFFT-inspired Quantum Shift-based Sequence Alignment and its Efficient Simulation on Decision Diagrams, by Yusuke Kimura and 1 other authorsView PDFHTML (experimental)TeX 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?)