Quantum Entanglement-Based Information Encoding and Time Series Prediction

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper investigates the potential of utilizing quantum entanglement for enhancing time series prediction. Traditional time series forecasting methods often struggle with complex, non-linear patterns and high-dimensional data. We propose a novel approach that leverages the unique properties of quantum entanglement to encode time series information and extract predictive insights. Specifically, we map time series data onto entangled quantum states, allowing us to access correlations and patterns that may be missed by classical methods. The theoretical framework outlines the key principles, including the mapping process, the measurement strategy, and the expected improvement in predictive accuracy. We present a mathematical model incorporating entanglement entropy and its influence on the prediction process. The core claim is that this entanglement-based encoding significantly improves time series prediction. The research highlights a fundamentally different approach to time series analysis, moving beyond classical information processing to exploit the quantum mechanical phenomenon of entanglement.

DOI: 10.5281/zenodo.22194489

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