Adaptive Quantum State Collapse for Machine Learning Training

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper investigates the application of adaptive quantum state collapse within the context of machine learning training. Traditional training methods often rely on static parameters, limiting the model's ability to adapt to complex data patterns. We propose a novel approach leveraging reinforcement learning to dynamically adjust the quantum state collapse process, optimizing model performance. The core mechanism involves a reinforcement learning agent that iteratively refines the collapse based on the model's output and data distribution, leading to improved generalization and convergence speed. This work presents a framework for dynamically shaping the quantum state to enhance machine learning capabilities. The potential for significant gains in model accuracy and training efficiency is a key focus.

DOI: 10.5281/zenodo.22474513

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