Title: Adaptive Quantum Sampling for Probability Density Estimation

Jincheng Zhang

Published Sep 1, 2026• 0 citations

Abstract

This paper explores the application of adaptive quantum sampling to probability density estimation, a critical task in various fields including physics, chemistry, and finance. Classical methods struggle with high-dimensional probability distributions, often requiring computationally intensive sampling techniques. We introduce a novel quantum algorithm that dynamically adjusts its sampling strategy based on observed data, significantly enhancing the accuracy of probability density estimation compared to traditional methods. The core mechanism leverages quantum circuits to implement a probabilistic sampling process, ensuring a more robust and efficient estimation of distributions. We detail the algorithm's architecture, key quantum operations, and demonstrate its effectiveness through simulations and illustrative examples. The resulting quantum algorithm holds the potential to unlock deeper insights into complex probability distributions, offering a significant advancement in statistical analysis.

DOI: 10.5281/zenodo.22223572

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