Quantum Neural Network for Time Series Forecasting: Harnessing Quantum Computing's Potential in Predictive Modeling
D. Balakrishnan, Umasree Mariappan, Pagadala Geetha Manikanta Raghavendra, P.K. Reddy, Rayavarapu Lakshmi Narasimha Dinesh, Shaik Bugganapalli Jabiulla · 2023
Time series forecasting is a critical problem with applications in various fields, such as finance, energy, and environmental science. While classical machine learning methods have shown significant progress in this domain, the advent of quantum machine learning introduces a new perspective to address the complexities of time series prediction. In this research, we propose a novel Quantum Neural Network (QNN) approach for time series forecasting, leveraging the unique capabilities of quantum computing to capture intricate temporal dependencies and exploit quantum parallelism. By encoding time series data into quantum states and designing quantum circuits for processing, the QNN aims to achieve improved predictive accuracy compared to classical neural networks. Through extensive experiments on diverse time series datasets, we demonstrate the potential of our QNN framework and discuss the future prospects of quantum-powered time series prediction in real-world applications.