Quantum-Enhanced Probability Distribution Algorithms

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

Quantum-enhanced probability distribution algorithms represent a significant advancement in computational modeling, particularly for complex probabilistic scenarios. This paper explores the potential of leveraging quantum mechanics to dramatically improve the accuracy and speed of Monte Carlo simulations, addressing the inherent limitations of classical approaches. The core claim is to develop a novel algorithm that utilizes quantum superposition and entanglement to efficiently explore the space of possible distributions, thereby reducing computational cost and enhancing performance. The proposed mechanism centers around exploiting quantum phenomena to overcome the computational bottlenecks encountered in traditional Monte Carlo methods, offering a paradigm shift in probabilistic modeling. This work will detail the theoretical foundations of the proposed algorithm, its implementation and testing with illustrative examples, and its potential impact on various applications.

DOI: 10.5281/zenodo.22156762

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