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[2602.14699] Qute: Towards Quantum-Native Database
Summary
The paper presents Qute, a quantum-native database that integrates quantum computation into database operations, enhancing performance over classical methods.
Why It Matters
As quantum computing advances, traditional databases may struggle to keep pace with data processing demands. Qute proposes a novel approach that leverages quantum capabilities, potentially revolutionizing data management and analytics in various fields, including AI and big data.
Key Takeaways
- Qute compiles SQL into quantum circuits for efficient execution.
- It employs a hybrid optimizer to choose between quantum and classical processing.
- Selective quantum indexing and fidelity-preserving storage are introduced to address qubit limitations.
- The paper outlines a roadmap for evolving quantum-native databases.
- Qute demonstrates superior performance on a real quantum processor compared to classical systems.
Computer Science > Databases arXiv:2602.14699 (cs) [Submitted on 16 Feb 2026] Title:Qute: Towards Quantum-Native Database Authors:Muzhi Chen, Xuanhe Zhou, Wei Zhou, Bangrui Xu, Surui Tang, Guoliang Li, Bingsheng He, Yeye He, Yitong Song, Fan Wu View a PDF of the paper titled Qute: Towards Quantum-Native Database, by Muzhi Chen and 9 other authors View PDF Abstract:This paper envisions a quantum database (Qute) that treats quantum computation as a first-class execution option. Unlike prior simulation-based methods that either run quantum algorithms on classical machines or adapt existing databases for quantum simulation, Qute instead (i) compiles an extended form of SQL into gate-efficient quantum circuits, (ii) employs a hybrid optimizer to dynamically select between quantum and classical execution plans, (iii) introduces selective quantum indexing, and (iv) designs fidelity-preserving storage to mitigate current qubit constraints. We also present a three-stage evolution roadmap toward quantum-native database. Finally, by deploying Qute on a real quantum processor (origin_wukong), we show that it outperforms a classical baseline at scale, and we release an open-source prototype at this https URL. Comments: Subjects: Databases (cs.DB); Artificial Intelligence (cs.AI); Hardware Architecture (cs.AR) Cite as: arXiv:2602.14699 [cs.DB] (or arXiv:2602.14699v1 [cs.DB] for this version) https://doi.org/10.48550/arXiv.2602.14699 Focus to learn more arXiv-issued DOI via DataCite ...
