Quantum Entanglement Encoding for Distributed Time Series Prediction

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

This paper explores the potential of utilizing quantum entanglement for distributed time series prediction. The core claim is that leveraging multi-quantum entanglement between nodes can enable more accurate predictions compared to traditional single-node approaches. We propose a mechanism involving the construction of an entangled quantum network, encoding time series data through entanglement, and employing quantum decoherence mitigation techniques to enhance prediction precision. This research introduces a novel paradigm by combining quantum entanglement with distributed time series prediction, offering a fundamentally different approach to forecasting. The theoretical framework presented outlines the key concepts and potential advantages of this method, focusing on the enhanced information processing capabilities offered by quantum mechanics. This work investigates the feasibility and potential improvements in prediction accuracy achievable through this entangled network.

DOI: 10.5281/zenodo.22332903

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