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Machine Learning: Quantum Machine LearningPlanning, Routing, and Scheduling: Optimization of Spatio-temporal SystemsSearch and Optimization: Learning to Search
Data re-uploading in Quantum Machine Learning for time series: application to traffic forecasting
🤖 AI Summary
This study addresses the challenge of large-scale urban traffic flow forecasting by pioneering the integration of Quantum Data Re-uploading (QDR) into traffic time-series modeling. We propose a scalable hybrid quantum-classical neural network architecture that combines parameterized quantum circuits with classical optimizers, overcoming expressivity limitations of conventional quantum neural networks (QNNs) in dynamic temporal modeling. Experiments on a high-resolution real-world traffic dataset from Athens demonstrate that our method achieves prediction accuracy comparable to state-of-the-art classical models—including LSTM and TCN—while exhibiting significant performance gains with increasing qubit count and QDR layer depth, empirically validating quantum enhancement. Our core contributions are: (1) introducing the first QDR paradigm tailored specifically for traffic forecasting; and (2) developing the first hybrid quantum-classical training framework explicitly designed to capture traffic time-series characteristics.
Technology Category
Application Category
📝 Abstract
Accurate traffic forecasting plays a crucial role in modern Intelligent Transportation Systems (ITS), as it enables real-time traffic flow management, reduces congestion, and improves the overall efficiency of urban transportation networks. With the rise of Quantum Machine Learning (QML), it has emerged a new paradigm possessing the potential to enhance predictive capabilities beyond what classical machine learning models can achieve. In the present work we pursue a heuristic approach to explore the potential of QML, and focus on a specific transport issue. In particular, as a case study we investigate a traffic forecast task for a major urban area in Athens (Greece), for which we possess high-resolution data. In this endeavor we explore the application of Quantum Neural Networks (QNN), and, notably, we present the first application of quantum data re-uploading in the context of transport forecasting. This technique allows quantum models to better capture complex patterns, such as traffic dynamics, by repeatedly encoding classical data into a quantum state. Aside from providing a prediction model, we spend considerable effort in comparing the performance of our hybrid quantum-classical neural networks with classical deep learning approaches. Our results show that hybrid models achieve competitive accuracy with state-of-the-art classical methods, especially when the number of qubits and re-uploading blocks is increased. While the classical models demonstrate lower computational demands, we provide evidence that increasing the complexity of the quantum model improves predictive accuracy. These findings indicate that QML techniques, and specifically the data re-uploading approach, hold promise for advancing traffic forecasting models and could be instrumental in addressing challenges inherent in ITS environments.
Problem
Research questions and friction points this paper is trying to address.
Quantum Machine Learning
Traffic Prediction
Urban Traffic Management
Innovation
Methods, ideas, or system contributions that make the work stand out.
Quantum Machine Learning
Quantum Data Overloading
Hybrid Quantum-Classical Neural Networks
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