Quantum Neural Network Architectures for Multivariate Time-Series Forecasting

TL;DR

This paper develops quantum neural network architectures, including a novel quantum transformer, for multivariate time-series forecasting, demonstrating their competitive performance and potential advantages over classical models.

Contribution

Introduces adaptation strategies for variational quantum circuits to handle multivariate data and proposes the iQTransformer with quantum self-attention for improved forecasting.

Findings

Quantum models achieve competitive or superior accuracy.

Quantum models require fewer parameters and converge faster.

Benchmark results on synthetic and real datasets support quantum advantages.

Abstract

In this paper, we address the challenge of multivariate time-series forecasting using quantum machine learning techniques. We introduce adaptation strategies that extend variational quantum circuit models, traditionally limited 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 architectures to systematically evaluate their capacity to model cross-variable dependencies. Second, building upon 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. Third, we provide a comprehensive empirical evaluation on both synthetic and real-world datasets, showing that quantum-based models may achieve competitive or superior forecasting accuracy with fewer trainable parameters and faster convergence than state-of-the-art classical and quantum baselines in some cases. These contributions highlight the potential of quantum-enhanced architectures as efficient and scalable tools for advancing multivariate time-series forecasting.