QCardEst/QCardCorr: Quantum Cardinality Estimation and Correction

2025-09-10

AI Summary

To address low cardinality estimation accuracy in database query optimization, this paper proposes QCardEst—the first end-to-end cardinality estimation algorithm based on quantum machine learning. Methodologically, it introduces a lightweight hybrid quantum-classical architecture: table-aware quantum state encoding (requiring only as many qubits as the number of tables), a single variational quantum circuit for compact SQL query representation, and the QCardCorr correction framework to post-process outputs from classical estimators. Its key contribution lies in pioneering the deployment of hardware-feasible quantum models for cardinality estimation—balancing practical quantum resource constraints with significant accuracy gains. Experimental evaluation on JOB-light and STATS benchmarks demonstrates that QCardEst outperforms the PostgreSQL query optimizer by 6.37× and 8.66×, respectively, and surpasses MSCN by 3.47× on JOB-light.

Technology Category

• Machine Learning: Quantum Machine Learning
• Data Mining & Knowledge Management: Intelligent Query Processing
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Security and Privacy: Large-scale security measurements

Abstract

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.

Problem

• Quantum cardinality estimation for SQL query optimization
• Hybrid quantum-classical network for processing database queries
• Improving classical estimators with quantum correction factors

Innovation

• Hybrid Quantum-Classical Network for cardinality estimation
• Compact quantum encoding for SQL queries
• Quantum correction factor improves classical estimators