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Qute: Towards Quantum-Native Database

arXiv 2602.14699 ↗First posted 16 Feb 2026cs.DB cs.AI cs.ARcited by 0

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Abstract (arXiv)

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 https://github.com/weAIDB/Qute.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 8 of its 43 references that are in the map. First posted on arXiv 16 Feb 2026.

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References · 8

  1. 2021A Theoretical Framework for Learning from Quantum Datacited by 7
  2. 2023An Empirical Evaluation of Columnar Storage Formatscited by 5
  3. 2015Multiple Query Optimization on the D-Wave 2X Adiabatic Quantum Computercited by 4
  4. 2021Quanto: Optimizing Quantum Circuits with Automatic Generation of Circuit Identitiescited by 4
  5. 2024Quantum Data Management in the NISQ Era: Extended Versioncited by 3
  6. 2025DiskJoin: Large-scale Vector Similarity Join with SSDcited by 2
  7. 2024Quantum Data Management: From Theory to Opportunitiescited by 1
  8. 2024First Tree-like Quantum Data Structure: Quantum B+ Treecited by 1

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