Work

Quantum vs. classical: a comprehensive benchmark study for predicting time series with variational quantum machine learning

Year: 2026

Type: article

Abstract: Variational quantum machine learning algorithms have been proposed as promising tools for time series prediction, with the potential to handle complex sequential data more effectively than classical approaches. However, their practical advantage over established classical methods remains uncertain. In this work, we present a comprehensive benchmark study comparing a range of variational quantum algorithms (VQAs) and classical machine learning models for time series forecasting. We evaluate their predictive performance on three chaotic systems across 27 time series prediction tasks of varying complexity, and ensure a fair comparison through extensive hyperparameter optimization. Our results indicate that, in many cases, quantum models struggle to match the accuracy of simple classical counterparts of comparable complexity. Furthermore, we analyze the predictive performance relative to the model complexity and discuss the practical limitations of VQAs for time series forecasting.

Source: Machine Learning Science and Technology

DOI: 10.1088/2632-2153/ae365f

Authors: Tobias Fellner, David A Kreplin, Samuel Tovey, Christian Holm

Institutions: University of Stuttgart, Fraunhofer Institute for Manufacturing Engineering and Automation, Heilbronn University

Language: English

Topic: Quantum Computing Algorithms and Architecture

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,000

Funders: Bundesministerium für Wirtschaft und Klimaschutz, Deutsche Forschungsgemeinschaft