Qute: Towards Quantum-Native Database

Muzhi Chen, Xuanhe Zhou, Wei Zhou, Bangrui Xu, Surui Tang +5 more
2/16/2026
cs.DBcs.AIcs.AR

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

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Cite this paper

@article{chen2026qute,
  title  = {Qute: Towards Quantum-Native Database},
  author = {Muzhi Chen and Xuanhe Zhou and Wei Zhou and Bangrui Xu and Surui Tang and Guoliang Li and Bingsheng He and Yeye He and Yitong Song and Fan Wu},
  year   = {2026},
  eprint = {2602.14699},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2602.14699v1}
}

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