Qute: Towards Quantum-Native Database
Fuente:
arXiv
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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Soggetti: | |
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| _version_ | 1866918340574838784 |
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| author | Chen, Muzhi Zhou, Xuanhe Zhou, Wei Xu, Bangrui Tang, Surui Li, Guoliang He, Bingsheng He, Yeye Song, Yitong Wu, Fan |
| author_facet | Chen, Muzhi Zhou, Xuanhe Zhou, Wei Xu, Bangrui Tang, Surui Li, Guoliang He, Bingsheng He, Yeye Song, Yitong Wu, Fan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_14699 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Qute: Towards Quantum-Native Database Chen, Muzhi Zhou, Xuanhe Zhou, Wei Xu, Bangrui Tang, Surui Li, Guoliang He, Bingsheng He, Yeye Song, Yitong Wu, Fan Databases Artificial Intelligence Hardware Architecture 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. |
| title | Qute: Towards Quantum-Native Database |
| topic | Databases Artificial Intelligence Hardware Architecture |
| url | https://arxiv.org/abs/2602.14699 |