Quantum State Collapse Analysis with Adaptive Probabilistic Models

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

Quantum state collapse is a cornerstone of quantum mechanics, representing the irreversible transition from a superposition of states to a definite state. This phenomenon fundamentally challenges classical intuition and presents a significant hurdle in quantum information theory. This research proposes a novel method for analyzing this collapse process, leveraging Bayesian inference to model the probability of distinct collapse outcomes and ultimately providing a more precise predictive framework. The core mechanism centers around the creation of adaptive probabilistic models, enabling a deeper understanding of collapse events and their associated consequences. The method's potential impact lies in enhancing our comprehension of quantum phenomena and offering a pathway towards more robust quantum algorithms and systems.

DOI: 10.5281/zenodo.22456126

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