NOTE: This paper is part of a 4-part research series (tetralogy) on database systems: structural trade-offs in modern data architectures and quantum-augmented techniques for robust query optimization and OLAP execution. Other parts (Zenodo DOIs):- 10.5281/zenodo.17797344- 10.5281/zenodo.17797276- 10.5281/zenodo.17796302 Research positioning:- This is an unpublished research manuscript intended for open dissemination and community feedback.- The work emphasizes explicit assumptions/limitations and model- or simulation-based evidence where applicable. Reproducibility (current status):- The current release focuses on the manuscript and experimental description.- Code/data packaging will be added in subsequent releases (new versions) as a reproducibility bundle. License:- Please see the license field of this Zenodo record for reuse terms. We propose QACE, a reliability-gated hybrid estimator that selectively invokes a quantum-assisted routine to mitigate cardinality estimation failures under skew and correlation. Reproducibility artifacts (this version):- results_summary.csv: aggregated results for 80 binds × 5 repeats (Baseline / CPU-DS / GPU-DS), including median/P99 latency, flip rate, and measurement cost.- hardware_env.txt: hardware/software environment used for the measurements (CPU/GPU/OS/Python).- runbook.md: short, step-by-step instructions describing how the measurements were produced (text-only). Note: This release provides summary-level artifacts for transparency. Additional code/data packaging may be added in a later version. Table1_force_generic_shots64.csv: Summary table used for the main results under generic-plan policy (shots=64). Table3_qae_shots_ablation_generic.csv: Shot-count ablation results for QAE simulation under generic-plan policy. part3_bind_summary_stage1GPU_MEASURED_force_generic_plan_gpu0p85ms_med.csv: Stage-1 GPU proxy summary (measured GPU overhead=0.85 ms, bind workload: 80×5).
NOTE: This paper is part of a 4-part research series (tetralogy) on database systems: structural trade-offs in modern data architectures and quantum-augmented techniques for robust query optimization and OLAP execution. Other parts (Zenodo DOIs):- 10.5281/zenodo.17797344- 10.5281/zenodo.17797276- 10.5281/zenodo.17796302 Research positioning:- This is an unpublished research manuscript intended for open dissemination and community feedback.- The work emphasizes explicit assumptions/limitations and model- or simulation-based evidence where applicable. Reproducibility (current status):- The current release focuses on the manuscript and experimental description.- Code/data packaging will be added in subsequent releases (new versions) as a reproducibility bundle. License:- Please see the license field of this Zenodo record for reuse terms. We propose QACE, a reliability-gated hybrid estimator that selectively invokes a quantum-assisted routine to mitigate cardinality estimation failures under skew and correlation. Reproducibility artifacts (this version):- results_summary.csv: aggregated results for 80 binds × 5 repeats (Baseline / CPU-DS / GPU-DS), including median/P99 latency, flip rate, and measurement cost.- hardware_env.txt: hardware/software environment used for the measurements (CPU/GPU/OS/Python).- runbook.md: short, step-by-step instructions describing how the measurements were produced (text-only). Note: This release provides summary-level artifacts for transparency. Additional code/data packaging may be added in a later version. Table1_force_generic_shots64.csv: Summary table used for the main results under generic-plan policy (shots=64). Table3_qae_shots_ablation_generic.csv: Shot-count ablation results for QAE simulation under generic-plan policy. part3_bind_summary_stage1GPU_MEASURED_force_generic_plan_gpu0p85ms_med.csv: Stage-1 GPU proxy summary (measured GPU overhead=0.85 ms, bind workload: 80×5).
NOTE: This paper is part of a 4-part research series (tetralogy) on database systems: structural trade-offs in modern data architectures and quantum-augmented techniques for robust query optimization and OLAP execution. Other parts (Zenodo DOIs):- 10.5281/zenodo.17797344- 10.5281/zenodo.17797276- 10.5281/zenodo.17796162 Research positioning:- This is an unpublished research manuscript intended for open dissemination and community feedback.- The work emphasizes explicit assumptions/limitations and model- or simulation-based evidence where applicable. Reproducibility (current status):- The current release focuses on the manuscript and experimental description.- Code/data packaging will be added in subsequent releases (new versions) as a reproducibility bundle. License:- Please see the license field of this Zenodo record for reuse terms. We analyze key-only offloading for OLAP bottlenecks (Top-K and join-related primitives) and derive break-even conditions under an explicit hybrid latency/cost model.
The rapidly increasing number of heterogeneous data ecosystems, such as structured databases, semi-structured data lakes, real-time streams, distributed knowledge graphs, has further complicated the issue of query planning and optimization in modern information systems extensively. The optimization of queries is essentially an NP-hard combinatorial problem especially when based on multi-join queries, distributed execution plan, dynamic cost model, and uncertainty in the data access patterns. Even decades of concept and engineering advances, classical database optimizers are rapidly becoming unable to offer a globally optimal execution plan when faced with high-dimensional data, high data volatility, with a very tight latency constraint. Good news Recent breakthroughs in quantum computing propose promising computational paradigms that can be used to solve combinatorial optimization problems via quantum parallelism, superposition, and entanglement. Nevertheless, even the near-term quantum hardware is limited in the number of available qubits, noise, and decoherence, making it impractical to achieve fully quantum database systems in any perceived future. This is also the reason why quantum-classical hybrid algorithms are being developed: designed to add quantum optimization subroutines to classical database architectures is guaranteed without breaking their compatibility with existing execution engines. The following paper presents a framework of scalable quantum-classical hybrid computing of complex query planning in heterogeneous data ecosystems. The framework breaks down query optimization into classical and quantum-amenable optimization, which allows NP-hard subproblems, such as join ordering, operator placement, and cost minimization, to be transformed into quantum optimization models such as Quadratic Unconstrained Binary Optimization (QUBO) and Ising Hamiltonians. Classical elements handle query parsing, semantic analysis, constraint verification, and orchestration, whereas quantum processors are called selectively to solve combinatorial decision-making tasks. The suggested architecture will consist of a layered execution, hybrid execution, cost-model encoding approach, and adaptive fallback mechanisms to make them robust to quantum hardware variability. This has been experimentally tested with simulated quantum annealing and variational quantum algorithms that exhibit a quantifiable improvement in the quality and scalability of optimization and heuristics based on entirely classical algorithms, especially in high-dimensional query plans. The findings confirm the practicability of quantum query optimization and put down a feasible route to implement quantum algorithms in the serving machine of the next-generation data management systems.
Quantum annealers offer a promising sampling-based computing paradigm for large-scale combinatorial optimization. However, directly using them to solve database optimization problems in real-time scenarios remains challenging due to strict hardware constraints and the opacity of commercial hybrid solvers. To address these challenges, we present QDBO , a white-box quantum-augmented database optimizer that directly leverages low-level quantum sampling on a quantum annealer. It first performs an initial reduction to produce embeddable QUBO instances via multiple mapping strategies that trade off efficiency and quality. It then applies an online iterative relaxation correction procedure that uses sampling feedback to correct the reduced QUBO across iterations, enabling controllable quality improvements under varying time budgets. We integrate QDBO with the PostgreSQL query optimizer to construct Q 2 O-QDBO for join order optimization. Evaluations on the Join Order Benchmark (JOB) and the Cardinality Estimation Benchmark (CEB) show that Q 2 O-QDBO improves 62 out of 113 JOB queries, and achieves up to 49.36× execution-time speedup and 36.88× end-to-end speedup on the CEB workload. Evaluations on synthetic queries with up to 50 relations show that Q 2 O-QDBO achieves significantly less end-to-end latency while maintaining similar solution quality compared with black-box quantum solvers. To demonstrate generality, we further integrate QDBO with PostgreSQL for index selection on TPC-H, where QDBO achieves up to 2.64× workload speedup over the empty-index baseline and outperforms classical and black-box solver baselines. These results demonstrate that using QDBO to solve large-scale database optimization problems in real-time settings is promising.
Recent advances in quantum computing have led to progress in exploring quantum applications across diverse fields, including databases and data management. This work presents a quantum machine learning model that tackles the challenge of estimating metrics, such as cardinalities, execution times, and costs, for SQL queries in relational databases. Precise estimations are crucial for the query optimizer to optimize query processing in relational databases efficiently. Our proposed quantum machine learning model consists of a novel query encoding mechanism, which maps SQL queries into high-dimensional Hilbert spaces using grammatical representations of the queries. The encoding mechanism translates SQL queries into parameterized quantum circuits, forming the core of the quantum machine learning model. The parameters in this model are tuned using standard quantum machine learning techniques. This encoding was first developed in quantum natural language processing (QNLP), and this work demonstrates its natural application in database optimization. Because the encoding mechanism is mathematically robust, the quantum machine learning model is also explainable, allowing us to draw a one-to-one correspondence between the elements in SQL queries and the model's parameters. The method is also scalable because it consists of multiple circuits, and we train and evaluate the model with hundreds of queries. Compared to previous research, our model achieves high accuracy, supporting the results obtained in the original QNLP research. We extend the previous QNLP work by adding 4-class and 8 -class classification tasks and comparing the cardinality estimation results with those from state-of-the-art databases. This model shows a competitive accuracy compared to the estimators in the databases. We theoretically analyze the quantum machine learning model by calculating its expressibility and entangling capabilities, which have been suggested as representative metrics describing the model's trainability and learnability features.
Cardinality estimation is an important part of query optimization in DBMS. We develop a Quantum Cardinality Estimation (QCardEst) approach using Quantum Machine Learning with a Hybrid Quantum-Classical Network. We define a compact encoding for turning SQL queries into a quantum state, which requires only qubits equal to the number of tables in the query. This allows the processing of a complete query with a single variational quantum circuit (VQC) on current hardware. In addition, we compare multiple classical post-processing layers to turn the probability vector output of VQC into a cardinality value. We introduce Quantum Cardinality Correction QCardCorr, which improves classical cardinality estimators by multiplying the output with a factor generated by a VQC to improve the cardinality estimation. With QCardCorr, we have an improvement over the standard PostgreSQL optimizer of 6.37 times for JOB-light and 8.66 times for STATS. For JOB-light we even outperform MSCN by a factor of 3.47.
This Zenodo record provides an independent, self-contained reproducibility package for the figures and tables reported in the accompanying manuscript on conditional entanglement bounds and ladder exact-diagonalization (ED) benchmarks for many-body localization. Contents Google Colab notebook (MBL_entropyflow_zenodo.ipynb) that reproduces: fig_entropy_flow.png (boundary-normalized mid-spectrum entanglement vs disorder strength) fig_drift.png (finite-size drift indicator Δ(W)) summary tables exported as CSV (entanglement and drift) Archived datasets (entropy_data_new.csv, and optional quench outputs if present) used to generate the figures and tables without rerunning ED. Optional ED script (compute_data.py) for regenerating datasets from scratch (computationally heavier; intended for users who wish to reproduce raw data). What the notebook reproduces Running the notebook generates: Figures in outputs/figures/ Tables in outputs/tables/ (CSV format) including: entropy_summary.csv drift_summary.csv Computational notes The notebook is designed to run end-to-end in Google Colab. Recomputing raw ED data can be time-consuming; the included CSVs allow immediate regeneration of the paper figures/tables. Results depend on system size (e.g., 4×2 and 5×2 ladders), disorder type (random vs quasiperiodic), and sampling parameters documented in the notebook. License Choose one of the following (recommended): MIT License (best for code + data reuse), or CC BY 4.0 (best if you want strict attribution for all materials). Citation If you use this package, please cite: The Zenodo DOI for this record (automatically generated by Zenodo), And the associated manuscript/preprint (add arXiv or journal reference here once available).