Title: Dynamic Quantum State Collapse with Adaptive Resonance

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Quantum state collapse remains a significant challenge in quantum computation, demanding intricate control and robust algorithms. Conventional collapse mechanisms often rely on predefined rules, limiting the algorithm's adaptability. This paper introduces a novel dynamic quantum state collapse algorithm leveraging an adaptive resonance network (ARN) to dynamically adjust the influence of the network on the collapse probability. The core mechanism focuses on learning a 'tuning' parameter for the collapse, allowing the algorithm to respond to the system's internal dynamics and achieve a more robust and adaptable outcome. We propose a method for training the ARN to optimize the collapse probability, resulting in a quantum computation that exhibits greater resilience to perturbations and subtle variations in the system's state. This work demonstrates the potential for significantly enhanced quantum computation through a fundamentally different approach to collapse management.

DOI: 10.5281/zenodo.22278588

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