# Qute: Towards Quantum-Native Database

arXiv: https://arxiv.org/abs/2602.14699 · HTML: https://mlsanity.com/paper/2602.14699/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 43 references that are in the map.
- First posted on arXiv 16 Feb 2026.

## Builds on (most cited first, 8 of 8)

- [A Theoretical Framework for Learning from Quantum Data](https://mlsanity.com/paper/2107.06406/) (2021)
- [An Empirical Evaluation of Columnar Storage Formats](https://mlsanity.com/paper/2304.05028/) (2023)
- [Multiple Query Optimization on the D-Wave 2X Adiabatic Quantum Computer](https://mlsanity.com/paper/1510.06437/) (2015)
- [Quanto: Optimizing Quantum Circuits with Automatic Generation of Circuit Identities](https://mlsanity.com/paper/2111.11387/) (2021)
- [Quantum Data Management in the NISQ Era: Extended Version](https://mlsanity.com/paper/2409.14111/) (2024)
- [DiskJoin: Large-scale Vector Similarity Join with SSD](https://mlsanity.com/paper/2508.18494/) (2025)
- [Quantum Data Management: From Theory to Opportunities](https://mlsanity.com/paper/2403.02856/) (2024)
- [First Tree-like Quantum Data Structure: Quantum B+ Tree](https://mlsanity.com/paper/2405.20416/) (2024)

## Abstract (arXiv)

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.