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Qute: Towards Quantum-Native Database
Muzhi Chen, Xuanhe Zhou, Wei Zhou, Bangrui Xu, Surui Tang, Guoliang Li, Bingsheng He, Yeye He, Yitong Song, Fan Wu
TL;DR
Quantum databases face challenges in mapping SQL to quantum primitives, optimizing noise-sensitive hybrid plans, operating under limited qubit capacity, and preserving stored-data fidelity. Qute addresses these challenges with integrated quantum-aware compilation, hybrid optimization, selective indexing, fidelity-aware storage, and a three-stage roadmap; its prototype executes filtering on a real 72-qubit noisy processor using 2000 shots per experiment.
Problem
Quantum-native databases must map SQL to quantum primitives, optimize across noise-sensitive quantum and classical operators, selectively access data under limited qubit budgets, and preserve quantum-active attributes with high fidelity.
Method
Qute integrates extended-SQL compilation into gate-efficient circuits, hybrid plan optimization, selective quantum indexing, fidelity-aware tensor-network storage, and a three-stage evolution roadmap.
Results
Qute’s prototype executes database filtering on origin_wukong’s real 72-qubit noisy QPU, using 2000 measurement shots per quantum experiment with averaged results.
Takeaways & Limitations
Qute presents an integrated design for practical quantum query execution and positions quantum-native databases as a staged evolution from co-processors to fully quantum-native systems.
Abstract
from arXiv · showhide
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.
1 Introduction
Qute frames quantum computation as a first-class execution substrate for databases, addressing challenges in quantum-aware query processing, optimization, indexing, and storage. The paper also proposes a three-stage roadmap from quantum assistance to fully quantum-native systems.
- Motivation: Quantum databases seek to accelerate workloads that increasingly challenge classical machines, including scans over unindexed analytical data.The motivation centers on leveraging quantum algorithms for database workloads rather than merely simulating quantum computation classically.
- Challenges: Qute addresses four challenges: quantum-compatible SQL operators, noise-sensitive hybrid optimization, limited qubit budgets, and fidelity-preserving quantum-active storage.These constraints affect compilation, plan selection, selective data access, and data representation during quantum execution.
- Contributions: Qute integrates extended-SQL compilation, hybrid quantum-classical optimization, selective quantum indexing, and fidelity-aware storage into one architecture.Its storage engine supports compressed tensor networks for quantum-active data.
- Evolution roadmap: The roadmap progresses from classical systems using quantum co-processors, through hybrid quantum-classical databases, to fully quantum-native storage, indexing, and processing.The third stage assumes scalable fault-tolerant hardware and permits all data, including multimodal formats, to reside in quantum memory.
2 Preliminary
The preliminary section introduces qubits, gates, and circuits as the computational basis for Qute, then explains Grover-style filtering as a quantum alternative to classical scans. Quantum search requires O(√(N/M)) iterations for M matching records and has a worst-case O(√N) evaluation bound.
- Quantum concepts: Qute relies on qubits, quantum gates, and quantum circuits as the core concepts supporting quantum database execution.These concepts provide the representation, transformations, and composed procedures used by quantum operators.
- Grover search: Grover search combines an oracle that phase-marks records satisfying a predicate with a diffusion operator that amplifies marked states.The diffusion operator reflects amplitudes about their mean to increase the probability of desired records.
- Filtering complexity: O(√(N/M)) iterations yield a matching record with high probability when M records match among N candidates.Here M denotes the matched-record count.
- Filtering complexity: The worst case M=1 requires O(√N) evaluations, contrasting with a classical linear scan requiring O(N) predicate evaluations.The comparison motivates quantum filtering over unindexed data.
3 System Overview
Qute combines quantum and classical execution through operator profiles, runtime adaptation, and state-centric storage. Its design minimizes quantum data loading while representing quantum-active attributes as compressed tensor networks with fidelity-aware access operations.
- Design principles: Qute minimizes quantum data loading by shrinking the quantum-active footprint through selective indexing, pre-filtering, and on-demand encoding.Its design also favors amplitude-level parallelism and fidelity-aware optimization.
- Hybrid optimization: The hybrid optimizer compares quantum and classical operators using latency, success probability, and approximation-error profiles.Execution plans retain both realizations for eligible operators and can switch paths at runtime.
- Runtime adaptation: At runtime, Qute responds to noise by increasing measurement shots, selecting more robust circuit variants, or falling back to classical execution.These adaptations are triggered when quantum quality or stability degrades.
- Quantum storage: Qute stores quantum-active attributes as compressed tensor networks that function as logical pages supporting bounded reconstruction and sampling under fidelity constraints.The storage engine exposes LOAD, SAMPLE, and REFRESH primitives while tracking entropy and noise.
4 Quantum-Accelerable Operators
Qute reformulates relational operators as quantum circuits tailored to filtering, similarity joins, and aggregation. These designs use Grover search, SWAP-test overlap estimation, and amplitude estimation while preserving relational semantics or recovering classical results through scaling.
- Operator design: Qute restructures database operators to exploit quantum parallelism while preserving relational semantics.Table 2 summarizes the resource and circuit characteristics of quantum-accelerable relational operators.
- Quantum data filtering: Qute maps row identifiers into a search space and compiles predicates into quantum oracles that mark satisfying rows for Grover amplification.Compound predicates use ancilla-assisted encoding and multi-controlled operations, followed by classical reconciliation and predicate rechecking.
- Quantum similarity join: Qute encodes vectors as quantum states so their inner product is reflected in state overlap, which the SWAP Test estimates from ancilla measurement probabilities.The probability Pr[ancilla = 0] = (1+|⟨x|y⟩|^2)/2 links the measurement outcome to the overlap.
- Quantum aggregation: Qute reformulates aggregation as probability estimation by mapping numeric values to a target qubit’s observation probability.Amplitude estimation and scaling by N and Vmax recover SUM(v) = N·Vmax·Pr[Good = 1].
5 Quantum-Aware Index Structure
Qute narrows multidimensional query processing through selective quantum probing, then chooses classical verification or quantum KD-tree search based on candidate-set size.
- Motivation: The design addresses limited qubit capacity by loading only a small, query-relevant fraction of data into quantum superposition.This motivates narrowing the query scope before quantum processing.
- Selective quantum probing: Qute probes selected dimensions with quantum B+-trees to identify candidate nodes before evaluating complete multidimensional predicates.The strategy selects the smallest returned result set as the candidate set.
- Classical post-filtering: When k_s = O(log N), Qute classically verifies the remaining d−1 dimensions at cost O(k_s·(d−1)).For q1, four rows retrieved through d1 are further filtered using d2.
- Quantum KD-tree search: When k_s is large, Qute escalates to a quantum multi-divided KD-tree that jointly indexes multiple dimensions before verification.Recursive partitioning and quantum-assisted probing reduce the candidate set for larger workloads.
6 Quantum-Aware Optimizer
Qute's optimizer models quantum operators as probabilistic, accuracy-bounded alternatives and generates plans that coordinate quantum and classical implementations.
- Optimization objective: Qute shifts optimization from latency minimization toward a speed–reliability trade-off for noisy quantum operators.Quantum operators are treated as probabilistic, accuracy-bounded primitives.
- Latency model: Execution time is estimated from a topology-aware, layerized circuit schedule using calibrated gate durations and control overheads.The model accounts for limited connectivity and SWAP-induced depth inflation.
- Noise model: Operator success probability is approximated as P_q = ∏_{k=1}^K p_k, so deeper or poorly routed circuits receive multiplicative degradation.This guides pruning of fragile plans.
- Hybrid execution: Each quantum operator retains a deterministic classical fallback, while approximation error is enforced as a constraint or penalty.Runtime observations update parameters and refine plan choices adaptively.
7 Preliminary Results
Qute's prototype keeps parsing, planning, and result reconciliation classical while offloading filtering to a real quantum backend on origin_wukong.
- Prototype implementation: The prototype supports database filtering operations and offloads data filtering to a quantum computing backend.The classical engine handles SQL parsing, query planning, and result reconciliation.
- Platform: The implementation uses QPanda3 v1.0 and runs on the origin_wukong platform.
- Evaluation setup: All quantum circuits execute on a noisy 72-qubit real QPU, with experiments using 2000 measurement shots by default.Results are averaged across measurements.
8 Evolution and Further Directions
The roadmap progresses from quantum assistance for classical systems to quantum-centric hybrid execution and finally quantum-native storage, indexing, and processing.
- Quantum-Assisted Database: S1 uses quantum computing as a co-processor while data remains in classical memory and only selective tasks enter quantum registers.Limited qubits, coherence, and loading costs constrain this stage; fallback mechanisms preserve correctness.
- Quantum-Centric Database: S2 adds greater quantum-memory capacity, hybrid classical–quantum indexing, and quantum operators for joins and aggregations.The supplied passage identifies this stage as the transition toward quantum-centric execution.
- Quantum-Native Database: S3 places structured, unstructured, and multimodal data in quantum memory for fully quantum-native storage, indexing, and processing.Fault-tolerant hardware and advanced encodings support this stage.
9 Related Work
Prior quantum-database research spans query processing, optimization, indexing, integration, scheduling, approximate processing, and data structures, but generally preserves classical data models and storage layers.
- Prior work covers query processing, query optimization, index selection, data integration, transaction scheduling, approximate query processing, and data structures.
- These approaches largely use quantum computation as an isolated acceleration step rather than integrating it across database layers.
10 Conclusion
The paper presents Qute as a quantum-native database integrating quantum computation across the full data-management stack. It combines compilation, hybrid optimization, selective indexing, fidelity-aware storage, practical quantum query execution, and a three-stage roadmap.
- Qute integrates quantum computation across the full stack, from query parsing to execution, as a foundational shift in data management.
- Qute addresses operator compilation, hybrid optimization, selective indexing, and fidelity-aware storage in one system design.
- The system provides a unified design intended to support practical quantum query execution rather than simulation-based approaches.
- The paper outlines a three-stage roadmap covering architectural milestones and future research opportunities for quantum data systems.