Quantum-Enhanced Sampling of Complex Distributions

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

Quantum computing promises revolutionary advancements across numerous fields, and sampling from complex probability distributions is a critical step in many scientific and engineering disciplines. Traditional methods often struggle with the exponential growth of possibilities, limiting the computational efficiency of these tasks. This research proposes a novel quantum-enhanced sampling approach designed to overcome these limitations, utilizing quantum algorithms to efficiently explore the space of possible distributions. The core mechanism centers around leveraging quantum superposition and entanglement to accelerate the identification of key patterns and correlations within complex data, ultimately leading to improved representation and analysis of uncertain data. This paper details the theoretical framework, algorithm implementation, and preliminary results demonstrating the potential of this approach to significantly enhance distribution sampling.

DOI: 10.5281/zenodo.22520482

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