{"ID":2879000,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.17500","arxiv_id":"2508.17500","title":"Exploring Quantum Bootstrap Sampling for AQP Error Assessment: A Pilot Study","abstract":"Error assessment for Approximate Query Processing (AQP) is a challenging problem. Bootstrap sampling can produce error assessment even when the population data distribution is unknown. However, bootstrap sampling needs to produce a large number of resamples with replacement, which is a computationally intensive procedure. In this paper, we introduce a quantum bootstrap sampling (QBS) framework to generate bootstrap samples on a quantum computer and produce an error assessment for AQP query estimations. The quantum circuit design is included in this framework.","short_abstract":"Error assessment for Approximate Query Processing (AQP) is a challenging problem. Bootstrap sampling can produce error assessment even when the population data distribution is unknown. However, bootstrap sampling needs to produce a large number of resamples with replacement, which is a computationally intensive procedu...","url_abs":"https://arxiv.org/abs/2508.17500","url_pdf":"https://arxiv.org/pdf/2508.17500v1","authors":"[\"Feng Yu\",\"Raya Jahan\"]","published":"2025-08-24T19:41:36Z","proceeding":"quant-ph","tasks":"[\"quant-ph\",\"math.ST\"]","methods":"[]","has_code":false}