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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2508.17500 |
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| _version_ | 1866909751286169600 |
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| author | Yu, Feng Jahan, Raya |
| author_facet | Yu, Feng Jahan, Raya |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_17500 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Exploring Quantum Bootstrap Sampling for AQP Error Assessment: A Pilot Study Yu, Feng Jahan, Raya Quantum Physics Statistics Theory 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. |
| title | Exploring Quantum Bootstrap Sampling for AQP Error Assessment: A Pilot Study |
| topic | Quantum Physics Statistics Theory |
| url | https://arxiv.org/abs/2508.17500 |