Forecasting demand for assets and services can be addressed in various markets, providing a competitive advantage when the predictive models used demonstrate high accuracy. However, the training of machine learning models incurs high computational costs, which may limit the training of prediction models based on available computational capacity. In this context, this paper presents an approach for training demand prediction models using quantum neural networks. For this purpose, a quantum neural network was used to forecast demand for vehicle financing. A classical recurrent neural network was used to compare the results, and they show a similar predictive capacity between the classical and quantum models, with the advantage of using a lower number of training parameters and also converging in fewer steps. Utilizing quantum computing techniques offers a promising solution to overcome the limitations of traditional machine learning approaches in training predictive models for complex market dynamics.
Forecasting demand for assets and services can be addressed in various markets, providing a competitive advantage when the predictive models used demonstrate high accuracy. However, the training of machine learning models incurs high computational costs, which may limit the training of prediction models based on available computational capacity. In this context, this paper presents an approach for training demand prediction models using quantum neural networks. For this purpose, a quantum neural network was used to forecast demand for vehicle financing. A classical recurrent neural network was used to compare the results, and they show a similar predictive capacity between the classical and quantum models, with the advantage of using a lower number of training parameters and also converging in fewer steps. Utilizing quantum computing techniques offers a promising solution to overcome the limitations of traditional machine learning approaches in training predictive models for complex market dynamics.
Cryptocurrency markets are known for their high volatility and complex price dynamics, which make accurate prediction and analysis extremely challenging. Traditional financial forecasting models and classical machine learning algorithms often struggle to capture the nonlinear and rapidly changing patterns present in cryptocurrency datasets. In recent years, advancements in artificial intelligence and quantum computing have opened new possibilities for analyzing complex financial data and improving prediction accuracy.This study proposes a quantum computing–based framework for cryptocurrency market prediction by integrating quantum machine learning techniques with financial time-series analysis. The proposed model utilizes quantum computing concepts such as quantum feature mapping, variational quantum circuits, and quantum recurrent neural networks to analyze cryptocurrency market data. Historical datasets containing information about cryptocurrency prices, trading volume, and market capitalization are used to train and evaluate the model.The proposed system aims to identify hidden patterns in cryptocurrency market trends and generate accurate predictions for future price movements and market volatility. The performance of the quantum-based model is compared with classical deep learning models such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks. Experimental results indicate that the quantum machine learning approach achieves improved prediction accuracy and lower forecasting error compared to traditional deep learning models.By leveraging the computational advantages of quantum computing, the proposed framework provides a powerful approach for analyzing highly complex financial datasets. The results demonstrate that quantum machine learning techniques have the potential to significantly enhance cryptocurrency market analysis, enabling more accurate forecasting and better decision-making for investors and financial analysts.
Time series forecasting is a critical problem with applications in various fields, such as finance, energy, and environmental science. While classical machine learning methods have shown significant progress in this domain, the advent of quantum machine learning introduces a new perspective to address the complexities of time series prediction. In this research, we propose a novel Quantum Neural Network (QNN) approach for time series forecasting, leveraging the unique capabilities of quantum computing to capture intricate temporal dependencies and exploit quantum parallelism. By encoding time series data into quantum states and designing quantum circuits for processing, the QNN aims to achieve improved predictive accuracy compared to classical neural networks. Through extensive experiments on diverse time series datasets, we demonstrate the potential of our QNN framework and discuss the future prospects of quantum-powered time series prediction in real-world applications.
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. We observe that, in fully connected network settings, hybrid quantum-classical models consistently underperform, with median scores approximately 10% worse than their purely classical counterparts across different configurations. In contrast, recursive architectures with data re-uploading show the opposite trend: hybrid models achieved up to 5% better median scores under comparable complexity settings. Additionally, these hybrid models converged in fewer training epochs, indicating improved training efficiency. 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.
Purpose The purpose of this study is to explore the potential for quantum computing to enhance applied machine learning tasks. The goal is to investigate whether quantum computing principles such as superposition, entanglement and quantum parallelism can improve predictive accuracy and efficiency in machine learning models. Design/methodology/approach To examine the potential, this study simulated a 4-qubit quantum processor on an Intel Core i5 8th generation central processing unit (CPU). The proposed model combines quantum principle and deep neural networks architecture involving quantum-forward and classical-backward processes; quantum part is in the forward pass, and the traditional neural network handles the backpropagation during model training. This model was tested using a real dataset to assess its practical applicability to machine learning tasks under real-world constraints. Findings The findings reveal the effectiveness of the quantum-classical hybrid approach which achieves an average area under the curve score of 92%, with a precision of 22.6% and a recall of 25.3%. These results suggest that, while quantum noise and Qubit limitations inherent to Noisy Intermediate-Scale Quantum systems impacted prediction accuracy and model generalizability, the quantum-forward process enhanced the model’s ability to identify true positive cases in a complex dataset. Compared to traditional approaches, this model addresses both the stability and predictive demands of highly imbalanced inventory data, although limitations remain in precision and scalability. Originality/value While many open questions remain about the real advantages of quantum computing in applied field of business, this study contributes to the feasibility of leveraging quantum machine learning for complex decision-making problems in supply chain management.
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.
We explore the efficacy of the novel use of parametrised quantum circuits (PQCs) as quantum neural networks (QNNs) for forecasting time series signals with simulated quantum forward propagation. The temporal signals consist of several sinusoidal components (deterministic signal), blended together with trends and additive noise. The performance of the PQCs is compared against that of classical bidirectional long short-term memory (BiLSTM) neural networks. Our results show that for time series signals consisting of small amplitude noise variations (up to 40 per cent of the amplitude of the deterministic signal) PQCs, with only a few parameters, perform similar to classical BiLSTM networks, with thousands of parameters, and outperform them for signals with higher amplitude noise variations. Thus, QNNs can be used effectively to model time series having, at the same time, the significant advantage of being trained significantly faster than a classical machine learning model in a quantum computer.