基于量子纠缠的多变量时间序列预测

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper explores the potential of leveraging quantum entanglement for improved multi-variable time series forecasting. Traditional time series methods often struggle with high dimensionality, nonlinearity, and complex dependencies within time series data. We propose a novel approach that maps time series data into a quantum state space, utilizing entangled qubits to capture intricate correlations. Through controlled manipulation of these entangled states and subsequent quantum measurements, we aim to extract predictive information. This research investigates the core claim that quantum entanglement can significantly enhance forecasting accuracy and robustness, particularly in scenarios with high dimensionality and complex relationships. The core mechanism involves mapping time series data to a quantum state space and utilizing entanglement to capture complex dependencies. The resulting predictions are obtained through quantum measurement and post-processing. This work offers a new paradigm for time series forecasting by harnessing the unique properties of quantum mechanics.

DOI: 10.5281/zenodo.22445993

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