Exploring Quantum Bootstrap Sampling for AQP Error Assessment: A Pilot Study

TL;DR

This paper introduces a quantum bootstrap sampling framework to efficiently generate bootstrap samples on quantum computers, aiming to improve error assessment in Approximate Query Processing despite computational challenges.

Contribution

It presents a novel quantum bootstrap sampling method and quantum circuit design for AQP error assessment, addressing computational intensity issues.

Findings

Quantum bootstrap sampling can generate bootstrap samples on quantum hardware.

The framework provides a new approach for AQP error estimation.

Potential for more efficient error assessment in large-scale data processing.

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.