A Quantum-Classical Hybrid Model for Long-term Network Traffic Prediction

Summary

QTSMixer, a hybrid quantum-classical model, enhances network traffic prediction by addressing TSMixer's limitations in periodic signals and long-term forecasts. It introduces quantum neural network components controlled by trainable parameters , outperforming TSMixer by 6.72% in long-term predictions on real-world data.

Abstract

Network traffic prediction, a critical application of time series analysis, is essential for network management and optimization. Traditional statistical models and machine learning methods have been employed for network traffic prediction, where recent advancements in multi-layer perception mixer architectures, particularly TSMixer, have achieved state-of-the-art performance. However, TSMixer faces challenges with periodic signals and long-term predictions. To address these limitations, we propose a Quantum TSMixer (QTSMixer) model, a hybrid quantum-classical approach that leverages quantum neural networks for enhanced feature extraction related to periodic signals and long-term dependencies. By introducing trainable parameters to control the strength of the quantum components, the hybrid structure of multi-layer perception and quantum neural network is generated. QTSMixer's cross-domain application potential and practical application capability are demonstrated through empirical analysis on real-world datasets, in which QTSMixer outperforms TSMixer by 6.72% in the long-term network traffic prediction dataset. In the future, the development of QTSMixer can be further explored in other fields, such as financial market analysis and weather prediction.

Introduction

Network Traffic Prediction (NTP)^1,2,3, as an essential application of time series analysis^4,5, involves analyzing historical network traffic data to identify patterns and trends, thereby forecasting future traffic changes. It is crucial for network management and optimization, as it can assist network operators in resource allocation, fault diagnosis, security monitoring, and quality of service assurance. The NTP problem has been well studied by statistical models and machine learning methods³.....

Protocol

QTSMixer methodology

Let be a multivariate time series of length sl and number of channels c. The multivariate forecasting task is defined as predicting future values

Results

We conducted a detailed empirical analysis on some datasets. The first experiment is based on the LTNTF dataset. The three cities exhibited distinct data distribution patterns: City A showed a significant low point in traffic flow at the beginning of the year; City B maintained relatively high values during the same period, while City C displayed more balanced distribution throughout the year, see Supplemental Figure S1, Supplemental Figure S2,.......

Discussion

In this paper, we proposed QTSMixer for long-term NTP, which is a novel quantum-classical hybrid approach with improvements in accuracy and cross-domain adaptability. This study not only expands the application potential of quantum computing in time series prediction but also points out the direction for its future deployment on real quantum hardware. However, the current quantum-classical hybrid architecture exhibits limited capability in processing exogenous variables. Future improvements could incorporate a channel-mi.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
DeepQuantum	TuringQ Co., Ltd.	https://deepquantum.turingq.com/	An efficient programming framework for quantum machine learning and optical quantum computing developed by TuringQ.
Qiskit	IBM	https://www.ibm.com/quantum/qiskit	An open-source SDK for working with quantum computers at the level of extended quantum circuits, operators, and primitives.
ibm_brisbane	IBM	https://quantum.ibm.com/	The superconducting quantum computer in the IBM Quantum Eagle family.
LTNTF dataset	China Mobile	https://jiutian.10086.cn/open/#/dataset/710012?platform=OpenInnovation	The dataset comes from the Jiutian AI platform of China Mobile.
python3.10	Python Software Foundation	https://www.python.org/downloads/release/python-3100/
quantum re-uploading technology	Pérez-Salinas, A., Cervera-Lierta, A., Gil-Fuster, E., Latorre, J. I. Data re-uploading for a universal quantum classifier. Quantum. 4 226 (2019).	https://doi.org/10.22331/q-2020-02-06-226
BasicTS+ framework	Shao, Z. et al. Exploring progress in multivariate time series forecasting: Comprehensive benchmarking and heterogeneity analysis. IEEE Transactions on Knowledge and Data Engineering. 37 291-305 (2023).	arXiv:2310.06119v2
GPU A100	NVIDIA		80G GPU
Transformers	Hugging Face	https://huggingface.co/	The platform where the machine learning community collaborates on models, datasets, and applications.