Title: Quantum Reinforcement Learning for High-Dimensional Time Series Forecasting

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper explores the application of quantum reinforcement learning (QRL) to the task of high-dimensional time series forecasting. Traditional forecasting methods often struggle with the curse of dimensionality and complex relationships within time series data. The proposed QRL framework leverages quantum computing's unique properties – superposition, entanglement, and quantum speedup – to address these challenges. We introduce a novel quantum reward mechanism designed to encourage exploration and learning of intricate temporal patterns, ultimately enhancing predictive accuracy and efficiency, especially when dealing with large datasets and intricate feature representations. This work presents a foundation for a paradigm shift in time series forecasting, promising significant improvements over existing methods.

DOI: 10.5281/zenodo.22203253

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