Qute：迈向量子原生数据库

Qute: Towards Quantum-Native Database

February 16, 2026

作者: Muzhi Chen, Xuanhe Zhou, Wei Zhou, Bangrui Xu, Surui Tang, Guoliang Li, Bingsheng He, Yeye He, Yitong Song, Fan Wu

cs.AI

摘要

本文提出一种量子数据库（Qute），将量子计算视为一等执行方案。与先前基于模拟的方法（在经典机器上运行量子算法或改造现有数据库以支持量子模拟）不同，Qute实现了四大创新：（一）将扩展版SQL编译为门高效的量子电路；（二）采用混合优化器动态选择量子与经典执行计划；（三）引入选择性量子索引机制；（四）设计保真度存储方案以缓解当前量子比特限制。我们还提出了量子原生数据库的三阶段演进路线图。通过在实际量子处理器（起源_悟空）上部署Qute，实验表明其在大规模场景下优于经典基准系统。开源原型已发布于https://github.com/weAIDB/Qute。

English

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.