Qute: Towards Quantum-Native Database
This work addresses the challenge of making quantum computation practical for database systems, which could benefit data-intensive applications, though it is incremental as it builds on existing quantum and database concepts.
The paper tackles the problem of integrating quantum computation into databases by proposing Qute, a quantum-native database that compiles extended SQL into quantum circuits and uses hybrid optimization to select execution plans. The result is a system that outperforms classical baselines at scale when deployed on a real quantum processor, with an open-source prototype released.
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.