Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Algorithms for intelligent systems

Time Series Forecasting Using Quantum Machine Learning Algorithms

Fathima Nihla Latheef, Rubell Marion Lincy G

Published Jan 1, 2025• 3 citations
Publisher
DOI
10.1007/978-981-97-6957-5_16

Related papers

Time Series Forecasting with Quantum Machine Learning Architectures

Mayra Alejandra Rivera-Ruiz, Andres Méndez-Vázquez, José Mauricio López-Romero

Lecture notes in computer science•Jan 1, 2022•25 citations

QuLTSF: Long-Term Time Series Forecasting with Quantum Machine Learning

Hari Hara Suthan Chittoor, Paul Griffin, Ariel Neufeld +2 more

Long-term time series forecasting (LTSF) involves predicting a large number of future values of a time series based on the past values. This is an essential task in a wide range of domains including weather forecasting, stock market analysis and disease outbreak prediction. Over the decades LTSF algorithms have transitioned from statistical models to deep learning models like transformer models. Despite the complex architecture of transformer based LTSF models `Are Transformers Effective for Time Series Forecasting? (Zeng et al., 2023)' showed that simple linear models can outperform the state-of-the-art transformer based LTSF models. Recently, quantum machine learning (QML) is evolving as a domain to enhance the capabilities of classical machine learning models. In this paper we initiate the application of QML to LTSF problems by proposing QuLTSF, a simple hybrid QML model for multivariate LTSF. Through extensive experiments on a widely used weather dataset we show the advantages of QuLTSF over the state-of-the-art classical linear models, in terms of reduced mean squared error and mean absolute error.

arXiv:2412.13769•International Conference on Agents and Artificial Intelligence (ICAART)•Jan 1, 2025•8 citations

Time Series Forecasting with Quantum Neural Networks

Manuel Pegalájar Cuéllar, M.C. Pegalajar, L. G. B. Ruiz +1 more

Lecture notes in computer science•Jan 1, 2023•3 citations

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

Tobias Fellner, David A. Kreplin, Samuel Tovey +1 more

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.

arXiv:2504.12416•Machine Learning Science and Technology•Jan 9, 2026•8 citations

Quantum deep neural networks for time series analysis

Anupama Padha, Anita Sahoo

Quantum Information Processing•May 24, 2024•12 citations

High-Dimensional Time Series Forecasting Models: A Quantum Adaptive Learning Approach

Jincheng Zhang

This paper explores the application of quantum self-adaptive learning to high-dimensional time series forecasting models, leveraging the unique properties of quantum computing to enhance prediction accuracy and efficiency, particularly in scenarios involving large datasets and complex feature representations. We propose a novel "Quantum Adaptive Adjustment" mechanism that dynamically adjusts the learning process based on the characteristics of the time series data, leading to improved model performance. The research focuses on mitigating the limitations of traditional machine learning algorithms in handling high dimensionality and complex patterns, presenting a promising approach for improved forecasting capabilities. The paper details the implementation of the quantum self-adaptive learning process, including the introduction of the adaptive adjustment mechanism and a comprehensive evaluation of its effectiveness through simulations and a benchmark dataset. We conclude with a discussion of the potential impact of this approach on various real-world time series forecasting tasks.

•Aug 31, 2026•0 citations
Long-term time series forecasting (LTSF) involves predicting a large number of future values of a time series based on the past values. This is an essential task in a wide range of domains including weather forecasting, stock market analysis and disease outbreak prediction. Over the decades LTSF algorithms have transitioned from statistical models to deep learning models like transformer models. Despite the complex architecture of transformer based LTSF models `Are Transformers Effective for Time Series Forecasting? (Zeng et al., 2023)' showed that simple linear models can outperform the state-of-the-art transformer based LTSF models. Recently, quantum machine learning (QML) is evolving as a domain to enhance the capabilities of classical machine learning models. In this paper we initiate the application of QML to LTSF problems by proposing QuLTSF, a simple hybrid QML model for multivariate LTSF. Through extensive experiments on a widely used weather dataset we show the advantages of QuLTSF over the state-of-the-art classical linear models, in terms of reduced mean squared error and mean absolute error.
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.
This paper explores the application of quantum self-adaptive learning to high-dimensional time series forecasting models, leveraging the unique properties of quantum computing to enhance prediction accuracy and efficiency, particularly in scenarios involving large datasets and complex feature representations. We propose a novel "Quantum Adaptive Adjustment" mechanism that dynamically adjusts the learning process based on the characteristics of the time series data, leading to improved model performance. The research focuses on mitigating the limitations of traditional machine learning algorithms in handling high dimensionality and complex patterns, presenting a promising approach for improved forecasting capabilities. The paper details the implementation of the quantum self-adaptive learning process, including the introduction of the adaptive adjustment mechanism and a comprehensive evaluation of its effectiveness through simulations and a benchmark dataset. We conclude with a discussion of the potential impact of this approach on various real-world time series forecasting tasks.