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DOIs):- 10.5281/zenodo.17797344- 10.5281/zenodo.17797276- 10.5281/zenodo.17796162 Research positioning:- This is an unpublished research manuscript intended for open dissemination and community feedback.- The work emphasizes explicit assumptions/limitations and model- or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.
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DOIs):- 10.5281/zenodo.17797344- 10.5281/zenodo.17797276- 10.5281/zenodo.17796162 Research positioning:- This is an unpublished research manuscript intended for open dissemination and community feedback.- The work emphasizes explicit assumptions/limitations and model- or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.
The join order (JO) optimization problem is a key challenge in query optimization. Classical approaches can compute the optimal solution for smaller queries. For larger queries, some heuristic methods trade off plan quality to reduce the exponential search space. Recently, quantum-based methods have been proposed to leverage quantum mechanisms to accelerate exploration; however, encoding problem-specific constraints as penalty terms introduces extra overhead. Moreover, quantum-inspired methods on classical hardware do not harness the true advantages of quantum computation. Furthermore, these methods remain at the simulation stage. In this demonstration, we present the first Quantum-augmented Query Optimizer ( Q 2 O ) that integrates a hybrid quantum-classical approach to solve the JO problem in a real database setup. This demonstration allows conference attendees to interact directly with Q 2 O by executing queries and viewing detailed execution results (Scenario 1). Users can easily compare plan quality between Q 2 O and PostgreSQL (Scenario 2). Additionally, they can experiment with quantum parameters to observe their impact (Scenario 3). Experimental results show that the query plan generated by our approach achieves up to a 13x speedup in query execution. The video corresponding to this demonstration is available at this link.
This record contains the Japanese and English v1 releases of the paper“κ-Theoretic Quantum Optimization as an Upper-Governance Principle,” togetherwith curated supplementary materials for reproduction. The paper positions κ-theory not as a stand-alone optimization trick but as anupper-governance principle that regulates when and how existing quantum-optimization levers should be activated. The empirical study is organized intothree directions: (A) whether the same performance can be reached with lowercost-to-target, (B) whether higher performance can be obtained under the sameoperational burden, and (C) whether one can construct a governed stack thatsurpasses existing stand-alone baselines in both performance and efficiency. Within the benchmark suite studied in the paper, the κ-governed optimizer showsa substantial advantage in Directions A and B, and the governed stack alsooutperforms stand-alone vendor-like baselines in Direction C for the includedsemi-real and hold-out cases. The paper further studies exploratory real-timeswitching among A/B/C modes and separates replay-based main evidence fromsynthetic operating-map supplementary evidence through an explicit provenancemap. This Zenodo record includes:- Japanese paper PDF/TeX (v1)- English paper PDF/TeX (v1)- Replay reproduction package (curated v2)- Synthetic operating-map package (curated v2)- Combined convenience bundle of the supplementary materials Supplementary bundle note:The “v2” suffix in the supplementary ZIP files denotes the curation version ofthe supplementary bundles and is independent of the paper version, which remainsv1. Related κ-theory background volumes:- Volume I of κ-Theory: A Local Comparison Framework for Gateway Anomaly and Higher-Family Law. DOI: 10.5281/zenodo.19254863- Volume II of κ-Theory: Structural Readings and Application-Side Interpretations Through Reduced Local-Form Matching. DOI: 10.5281/zenodo.19255089
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).
Mirror of Zenodo record 19655682. Zenodo DOI: 10.5281/zenodo.19655682 Version: 1.2 License: cc-by-4.0 Original record: https://zenodo.org/records/19655682 Zenodo description: Version 1.2.0 is a major restructuring of the Quantum-Safe Migration Playbook with three goals: make the content usable as a retrieval-augmented AI knowledge base, tighten evaluation discipline, and update all dated facts to April 2026. What's new in v1.2.0 Structural changes Fact-ID indexing: every atomic claim now has a stable §N.M identifier for precise cross-reference and citation. Explicit scope block at the top of the corpus, listing what's covered and — critically — what's out of scope. This is what allows a downstream AI advisor to refuse out-of-scope questions instead of improvising. Cross-reference lines at the end of each section pointing to related fact IDs. Provenance block: every dated or numeric claim in the corpus is listed at the bottom with its source and a re-check cadence. Content updates (April 2026) §1.2: NIST FIPS 203 (ML-KEM), FIPS 204 (ML-DSA), FIPS 205 (SLH-DSA) finalized August 13, 2024; HQC selected March 2025; FIPS 206 (FN-DSA/FALCON) in development. §1.3: NSA CNSA 2.0 timelines specified by category — software/firmware signing and network equipment by 2030; browsers, servers, cloud, and operating systems by 2033. §1.4 (new): compressed Q-Day estimates. Neutral-atom architectures and improved error correction have cut the qubit count needed to break P-256. Cloudflare now targets 2029 for full internal PQ readiness. §2.1: Cloudflare reports 52% of human-initiated web traffic using post-quantum hybrid key agreement (December 2025 data), up from 29% at the start of the year. §2.3 (new): the authentication gap. Key agreement is largely solved; public PQ certificates are not yet broadly available; CA/Browser Forum has not finalized the hybrid certificate format. §4: replaced unsourced latency percentage with mechanism-based description. §6.4 (new): Cryptographic Bill of Materials (CBOM) concept. §9.3: sharpened vendor validation criteria with FIPS 140-3 and NIST CAVP references. Evaluation suite Expanded from 4 queries to 15 golden queries plus 6 distractor queries. Each query has explicit pass AND fail criteria, testable against paraphrased responses. Distractor queries test scope enforcement: QKD, quantum physics, vendor recommendations, exact Q-Day predictions, legal advice, general crypto tutorials. Integration guide (new) Dedicated INTEGRATION.md with a ready-to-paste system prompt for a retrieval-augmented runtime. Ingestion guidance (corpus fits in system context for models with ≥8K windows; chunking patterns for larger deployments). Refresh cadence tied to the Provenance block. Version history v1.1.0 — initial Zenodo release. v1.2.0 — restructured, fact-indexed, expanded evaluation suite, updated to April 2026 standards and deployment data.