Qute: Towards Quantum-Native Database
AI 摘要
Qute提出了一种量子原生数据库,利用量子计算加速数据处理,并优化量子资源利用。
主要贡献
- 扩展SQL编译为量子电路
- 混合优化器动态选择执行计划
- 选择性量子索引
- 保真度存储
方法论
论文提出了一种量子数据库框架,通过编译优化、混合执行、索引和存储等技术,实现高效的量子数据处理。
原文摘要
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.