Year: 2026
Type: article
Abstract: In this paper, we address the challenge of multivariate time-series forecasting through quantum-enhanced machine learning architectures. We propose adaptation strategies that extend variational quantum circuit models (VQC), traditionally constrained to univariate data, toward the multivariate setting, exploring both purely quantum and hybrid quantum-classical formulations. First, we extend and benchmark several VQC-based and hybrid models to systematically evaluate their ability to capture cross-variable dependencies. Building on these foundations, we introduce the iQTransformer, a novel quantum transformer architecture that integrates a quantum self-attention mechanism within the iTransformer framework, enabling a quantum-native representation of inter-variable relationships. Finally, we present a comprehensive empirical evaluation on both synthetic and real-world datasets, demonstrating that quantum-enhanced models can achieve competitive or superior forecasting accuracy with fewer trainable parameters and faster convergence than state-of-the-art classical and quantum baselines. These findings highlight the potential of quantum-enhanced architectures as efficient and scalable tools for advancing multivariate time-series forecasting.
Source: The Journal of Supercomputing
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,290
Funders: Gobierno del Principado de Asturias, Ministerio de Ciencia, Innovación y Universidades, Instituto Tecnológico y de Energías Renovables, European Commission, Universidad de Oviedo, Ministerio de Asuntos Económicos y Transformación Digital, Gobierno de España, Cabildo de Tenerife, HORIZON EUROPE Framework Programme, Agencia Estatal de Investigación
Awards: G103507813, QCIRCLE Center of Excellence: ACCELERATING QUANTUM IMPACT ON SUSTAINABILITY, G1105844351, G1175115578, G1903309452, G1971932983, G2262748287, G2601397997, G300979063, G3159255893, G3480869486, G3681454997, G4042783231, G425668148, G491712853, G5080475149, G5195188938, G5209569390, G7084143925, G7233839064, G7805626310, G7863371703, G8497050386
OpenAlex ID: W7128608419