Title: Modular Quantum State Collapse for Time-Series Analysis

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper explores the application of a novel modular quantum state collapse framework for time-series analysis. Traditional methods often struggle with the inherent complexity of time-series data, leading to instability and unreliable models. We propose a quantum-inspired approach where the collapse of a quantum state is dynamically modulated by a modular structure, enabling robust and adaptive analysis. The core mechanism leverages a layered collapse process, where each layer adjusts its influence based on the observed data characteristics. This allows for the generation of more resilient and accurate time-series models compared to conventional techniques. The paper details the theoretical underpinnings, the implementation of the modular structure, and preliminary results demonstrating the effectiveness of this approach. We aim to provide a fundamentally new method for time-series data analysis that addresses limitations inherent in existing approaches.

DOI: 10.5281/zenodo.22195331

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