Qute brings quantum computing into database systems as a native execution substrate, not an add-on.
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.
Qute integrates quantum computation as a first-class substrate throughout the entire database query pipeline, not just as an isolated accelerator.
Quantum filtering using Grover's algorithm achieves O(√N) complexity instead of classical O(N), dramatically reducing computational checks needed.
The system rethinks the entire stack from SQL compilation through storage to preserve quantum state fidelity and optimize execution.
Limited qubit budgets restrict quantum execution to probing only small fractions of datasets during operation.
Quantum operators have fundamentally different latency, success probability, and approximation error characteristics requiring new optimization strategies.
Zonglin Yang, Lidong Bing
While large language models (LLMs) show promise in scientific discovery, existing research focuses on inference or feedback-driven training, leaving the direct modeling of the generative reasoning pro...
Jiaxuan Lu, Ziyu Kong +11
The central challenge of AI for Science is not reasoning alone, but the ability to create computational methods in an open-ended scientific world. Existing LLM-based agents rely on static, pre-defined...
Mert Yuksekgonul, Daniel Koceja +9
How can we use AI to discover a new state of the art for a scientific problem? Prior work in test-time scaling, such as AlphaEvolve, performs search by prompting a frozen LLM. We perform reinforcement...
Dingkun Liu, Yuheng Chen +6
Electroencephalography (EEG) foundation models have recently emerged as a promising paradigm for brain-computer interfaces (BCIs), aiming to learn transferable neural representations from large-scale ...
Jian Zhang, Yu He +6
Scientific reasoning relies not only on logical inference but also on activating prior knowledge and experiential structures. Memory can efficiently reuse knowledge and enhance reasoning consistency a...
