{"ID":2872728,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.08817","arxiv_id":"2509.08817","title":"QCardEst/QCardCorr: Quantum Cardinality Estimation and Correction","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.","short_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 t...","url_abs":"https://arxiv.org/abs/2509.08817","url_pdf":"https://arxiv.org/pdf/2509.08817v1","authors":"[\"Tobias Winker\",\"Jinghua Groppe\",\"Sven Groppe\"]","published":"2025-09-10T17:49:06Z","proceeding":"quant-ph","tasks":"[\"quant-ph\",\"cs.AI\",\"cs.DB\",\"cs.LG\"]","methods":"[]","has_code":false}