Qute：邁向量子原生數據庫

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

本文提出了一種將量子計算視為一等執行選項的量子數據庫（Qute）。有別於先前基於模擬的方法——要麼在經典機器上運行量子算法，要麼改裝現有數據庫進行量子模擬——Qute實現了四大創新：(i) 將擴展版SQL編譯為門高效的量子電路；(ii) 採用混合優化器動態選擇量子與經典執行方案；(iii) 引入選擇性量子索引機制；(iv) 設計保真度存儲方案以緩解當前量子位限制。我們還提出了通往量子原生數據庫的三階段演進路線圖。通過在真實量子處理器（起源·悟空）上部署Qute，實驗表明其在大規模場景下優於經典基準方案。我們已在https://github.com/weAIDB/Qute開源原型系統。

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.