Algorithmic Quantum State Collapse Optimization (AQSCO)

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

This paper introduces Algorithmic Quantum State Collapse Optimization (AQSCO), a novel algorithm designed to enhance the efficiency of quantum measurements through a synergistic combination of statistical likelihood and quantum interference. Traditional collapse methods often rely on deterministic approaches, potentially leading to suboptimal results. AQSCO addresses this limitation by dynamically adjusting the collapse process based on probability distributions, optimizing for reduced measurement error. The core mechanism utilizes a 'statistical collapse gradient' technique, effectively guiding the quantum state towards a more stable and predictable outcome. This approach offers the potential for significantly improved measurement accuracy and a deeper understanding of quantum phenomena. We present a comprehensive theoretical framework and preliminary experimental validation demonstrating the efficacy of AQSCO in a series of quantum state collapse experiments.

DOI: 10.5281/zenodo.22613523

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