Qute: Towards Quantum-Native Database

Muzhi Chen; Xuanhe Zhou; Wei Zhou; Bangrui Xu; Surui Tang; Guoliang Li; Bingsheng He; Yeye He

cs.DBFeb 16, 2026

Qute: Towards Quantum-Native Database

Authors: Muzhi Chen, Xuanhe Zhou, Wei Zhou, Bangrui Xu, Surui Tang, Guoliang Li, Bingsheng He, Yeye He,

Organizations: Shanghai Jiao Tong University · Tsinghua University · National University of Singapore · Microsoft Corporation ·

Abstract

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.

Figures & tables

Figure 1. Potential Evolution of Quantum Databases.

Component	Classical Database	Quantum Database
Compiler	Generate efficient machine code for classic execution (CPUs, GPUs, …).	Generate circuits executable directly on the quantum backend.
Operator	Perform tuple-at-a-time or vectorized processing (CPU cost $\downarrow$ ).	Replace tuple scans with amplitude and interference operations.
Plan Optimizer	Select the lowest-cost deterministic execution path.	Dynamically balance quantum speedup vs. noise / success rate.
Data Indexing	Accelerate lookup operations.	Shrink the candidate set that must
		be loaded into quantum computer.
Data Format	Byte-addressable and easy to parse.	Encode data in qubit states.

Table 1. Classical vs. Quantum Database Components.

Figure 2. Example Quantum Database System Design.

Type	Operator (SQL)	Typical Scenario	Candidate Quantum Algorithm	Complexity		Cost Model
				Quantum	Classical (without index)	Qubit Num	Gate Depth (per-circuit call)
Filter	Equality Filter	WHERE column = value	Grover ( Grover, 1996b ) (Search)	$O(\sqrt{N})$	$O(N)$	$n+a_{\mathrm{orc}}$	$O\!\left((D_{\mathrm{orc}}+D_{\mathrm{diff}})\cdot\sqrt{N/M}\right)$
	Range Filter	WHERE column > value	Grover ( Grover, 1996b ) (Threshold Oracle)	$O(\sqrt{N})$	$O(N)$	$n+b+a_{\mathrm{cmp}}$	$O\!\left((D_{\mathrm{cmp}}(b)+D_{\mathrm{diff}})\cdot\sqrt{N/M}\right)$
	LIKE	WHERE column LIKE ‘val%’	Grover ( Grover, 1996b ) (Prefix-Match Oracle)	$O(\sqrt{N})$	$O(N)$	$n+b+a_{\mathrm{pref}}$	$O\!\left((D_{\mathrm{pref}}(b)+D_{\mathrm{diff}})\cdot\sqrt{N/M}\right)$
	EXISTS	Existence check	Grover ( Grover, 1996b ) (Quantum Counting)	$O(\sqrt{N})$	$O(N)$	$n+a_{\mathrm{orc}}$	$O\!\left((D_{\mathrm{orc}}+D_{\mathrm{diff}})\cdot\sqrt{N}\right)$
Join	Equi-join	column1 = column2	Grover ( Grover, 1996b ) (Index Probing)	$O(\sqrt{N_{2}})$ / probe	$O(N_{1}\cdot N_{2})$	$n_{2}+b_{\mathrm{key}}+a_{\mathrm{orc}}$	$O\!\left((D_{\mathrm{idx}}+D_{\mathrm{key}}+D_{\mathrm{diff}}(n_{2}))\cdot\sqrt{N_{2}}\right)$ /probe

Table 2. Quantum-Accelerable Relational Operators (

M

and

N

are total and qualifying row numbers;

d

is the vector dimension;

\epsilon

is precision;

n

and

q_{*}

are qubit counts for data and precision registers;

a_{*}

is ancilla;

b

is bandwidth;

D

is the gate depth).

Figure 3. CSWAP-Gate Based Inner Product Calculation. \sysInner Product

Figure 4. Quantum-Aware Multi-Dimensional Index.

Figure 5. Performance Comparison ( Qute vs. Classic).

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Differentiable quantum architecture search (DQAS) is a promising framework for the automated design of quantum circuits, particularly for variational quantum optimization algorithms. However, its practical deployment on quantum hardware is limited by the large number of circuit measurements required during optimization, making hardware execution costly. In this work, we show that for a broad class of combinatorial optimization problems and commonly used rotational gate parameterizations, the measurement cost of DQAS can be significantly reduced without changing the optimization objective. We derive the proposed measurement reduction scheme theoretically and validate it experimentally on 3-SAT and MaxCut benchmark problems. Our approach reduces the requested gradient measurement cost by about 39 to 41% while introducing only negligible classical post-processing overhead, lowering the practical cost of executing DQAS on quantum hardware.

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