Quantum Adaptive Probability Model Optimization (QAPMO)

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

This paper introduces the Quantum Adaptive Probability Model Optimization (QAPMO) framework, a novel approach to probabilistic model optimization leveraging quantum entanglement. We propose a quantum-enhanced optimization algorithm that utilizes the instantaneous nature of entanglement to accelerate the search for optimal probability models. The core mechanism involves leveraging entangled quantum states to rapidly compute probability distributions, thereby improving model accuracy compared to classical optimization techniques. The paper explores the potential of QAPMO to address limitations in traditional optimization methods and offers a promising path towards more efficient probabilistic model design.

DOI: 10.5281/zenodo.22253554

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