With the wide application of 5G technology, the global Internet scale has grown sharply, putting forward higher requirements for both traditional network traffic prediction technology and network traffic prediction technology based on deep learning. Especially when dealing with large-scale data, the computational load and accuracy often fail to meet the actual demands. To overcome this problem, this paper introduces quantum technology and for the first time explores the challenge of applying quantum generative adversarial networks to network traffic prediction. Due to the advantage of efficient data processing capabilities of quantum generative adversarial networks (QGAN) and the sensitivity of long short-term memory networks (LSTM) to time series, the combination of the two provides a new research direction and application potential for time series analysis and prediction in the quantum field. This paper proposes a quantum generative adversarial network with an LSTM layer added to the generator. The temporal features extracted by LSTM are repeatedly embedded into the quantum circuit of the generator by using Angle encoding and data re-upload strategies. This enables quantum generative adversarial networks to handle temporal dependencies and enhances the expressive power of quantum circuits. Prediction experiments were conducted on the simulation platform using real network traffic data and compared with existing quantum neural network models for time series prediction such as QLSTM. The MAPE, $\mathbf{R}^{\mathbf{2}}$, MAE and RMSE indicators reached $3.4 \%, 98.6 \%, 0.018$ and 0.027 respectively, all showing significant advantages.
With the wide application of 5G technology, the global Internet scale has grown sharply, putting forward higher requirements for both traditional network traffic prediction technology and network traffic prediction technology based on deep learning. Especially when dealing with large-scale data, the computational load and accuracy often fail to meet the actual demands. To overcome this problem, this paper introduces quantum technology and for the first time explores the challenge of applying quantum generative adversarial networks to network traffic prediction. Due to the advantage of efficient data processing capabilities of quantum generative adversarial networks (QGAN) and the sensitivity of long short-term memory networks (LSTM) to time series, the combination of the two provides a new research direction and application potential for time series analysis and prediction in the quantum field. This paper proposes a quantum generative adversarial network with an LSTM layer added to the generator. The temporal features extracted by LSTM are repeatedly embedded into the quantum circuit of the generator by using Angle encoding and data re-upload strategies. This enables quantum generative adversarial networks to handle temporal dependencies and enhances the expressive power of quantum circuits. Prediction experiments were conducted on the simulation platform using real network traffic data and compared with existing quantum neural network models for time series prediction such as QLSTM. The MAPE, R2, MAE and RMSE indicators reached 3.4%,98.6%,0.018 and 0.027 respectively, all showing significant advantages.
This study proposes a novel hybrid prediction model (QGCN-LSTM) that combines Quantum Graph Convolutional Networks (QGCN) with classical Long Short-Term Memory (LSTM). The model takes space-time data as input and achieves quantum information conversion through a quantum encoding layer. Multi-scale features are extracted through the collaborative computation of QGCN and quantum gated loop units, and a quantum attention module is introduced to dynamically screen key information. Finally, the prediction results are generated through quantum measurement and a classical output layer. In the space-time data prediction task of urban traffic flow, a benchmark model system covering classical, cutting-edge, and traditional architectures was constructed. The experimental results show that QGCN-LSTM utilizes quantum entanglement gates to establish non-local road network associations, dynamically allocate feature weights to enhance the impact of critical time steps, and achieves deep compression of lines through quantum line pruning technology, effectively alleviating the common problem of “poor plateau” in quantum neural network training. In terms of prediction accuracy, the average absolute error (MAE) of its key hub nodes is reduced by 34.1% compared to the graph convolution LSTM (GCN-LSTM) model, and the Spatial Correlation Index (SCI) is improved to 0.89. In addition, it also shows excellent performance in dynamic response, edge computing efficiency, and other aspects, meeting the real-time requirements of the traffic signal control system. This study provides an effective paradigm for the application of quantum collaborative architecture in complex spatiotemporal prediction tasks.
Merger of Quantum computing and Machine learning explores a new shift in artificial intelligence. Quantum neural networks and parameterized quantum circuits are tools that enable the merger of these two branches. Here, we use Quantum circuit enabled long short term memory (QLSTM) neural network to forecast time series data. We show the efficacy of quantum computing by conducting experiments on two different time series data sets. In our first experiment, we are predicting the amount of rainfall fall and the second experiment has electric load (power) prediction. Our dataset for rainfall prediction includes hourly information on the weather conditions i.e., wind speed, wind direction, minimum and maximum temperatures, and pressure with the amount of rain falls. For electric load (power generation) dataset, few of the features include amount of biomass, amount of fossil brown coal/ignite, amount of fossil coal derived gas, nuclear power, solar power, and the corresponding wind velocity forecasts. We compare the training as well as the test loss of classical Bidirectional LSTM (BILSTM) and the Quantum BILSTM and observe that LSTM based on quantum approach reduces both the training and test loss considerably when compared to its classical part with very few epochs of training.
Quantum machine learning (QML) is a cross-disciplinary subject made up of two of the most exciting research areas: quantum computing and classical machine learning (ML), with ML and artificial intelligence (AI) being projected as the first fields that will be impacted by the rise of quantum machines. Quantum computers are being used today in drug discovery, material & molecular modelling and finance. In this work, we explore emerging research areas in the application of hybrid classical quantum machine learning (QML) in finance. We focus on hybrid classical-quantum models that have gained significant interest in the financial world for various applications. We utilise a real-world cryptocurrency dataset to train a quantum generative adversarial networks (qGAN), and implement the real-life NVIDIA stock price data on quantum LSTM (QLSTM), comparing its performance with classical Long Short Term Memory (LSTM) model. Additionally, we investigate the application of quantum circuit Born machine (QCBM) using a toy dataset for applications in finance. For the qGAN, we define quantum circuits for generators and showcase the potential for future quantum advantage in finance through QML, possibly in combination with QLSTM and QCBM. Our findings suggest that, while training classical data is more efficient on LSTM, the test predictions of QLSTM is slightly superior to its classical counterpart making it a promising candidate for financial forecasting and analysis for timeseries type data.
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.
This paper investigates the application of Quantum Generative Adversarial Networks (QGANs) for stock price prediction. Financial markets are inherently complex, marked by high volatility and intricate patterns that traditional models often fail to capture. QGANs, leveraging the power of quantum computing, offer a novel approach by combining the strengths of generative models with quantum machine learning techniques. We implement a QGAN model tailored for stock price prediction and evaluate its performance using historical market data. Results demonstrate that QGANs can generate synthetic data closely resembling actual market behavior, leading to enhanced prediction accuracy. The experiment was conducted using stock index price data and the AWS Braket SV1 simulator for training QGAN circuits. The quantum-enhanced model outperforms classical LSTM and GAN models in both convergence speed and prediction accuracy. This research marks a key step toward integrating quantum computing in financial forecasting, offering potential advantages in speed and precision over traditional methods. These findings hold promising implications for traders, financial analysts, and researchers.
This paper investigates the application of Quantum Generative Adversarial Networks (QGANs) to data augmentation for machine learning. Traditional Generative Adversarial Networks (GANs) rely on classical neural networks, while this research explores leveraging quantum circuits as generators within a QGAN architecture. The core idea is to train a quantum generator to produce synthetic data samples, supplementing existing datasets with novel instances. The discriminator remains a classical neural network, tasked with distinguishing between real and generated data. We present a theoretical framework for this approach, outlining the mathematical formulation and key components. Specifically, we introduce the notation (x, s_1, s_2, ..., s_K) to represent data samples, generator states, and relevant parameters. The objective function for the QGAN is defined as minimizing the Jensen-Shannon divergence between the real and generated data distributions. The effectiveness of this approach is discussed, highlighting potential advantages and challenges. This work contributes to the emerging field of quantum machine learning and offers a novel strategy for improving the performance of machine learning models, particularly in scenarios with limited training data.