Title: Quantum State Vector Collapse with Adaptive Probability Distribution

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

Quantum state vector collapse is a fundamental challenge in quantum measurement, often leading to errors and instability. This paper explores a novel quantum state vector collapse algorithm that leverages adaptive probability distributions to mitigate the energy required for collapse. We propose a mechanism where the algorithm dynamically adjusts the probability distribution based on the system's state and environment, effectively minimizing the "collapse energy" and promoting more stable and precise quantum measurements. The core claim is that this approach will significantly improve the reliability and efficiency of quantum measurement by reducing the inherent instability associated with traditional collapse methods. This work introduces a novel approach to quantum state vector collapse, offering a potential pathway toward more robust and accurate quantum experiments.

DOI: 10.5281/zenodo.22511994

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