Quantum machine learning has been shown to offer exponential computational boosts compared to current classical models through the exploitation of quantum mechanical properties such as entanglement and superposition. The finance sub field of fin-tech, where financial services are constructed around a customer's demands, is believed to benefit with the use of hybrid quantum-classical machine learning algorithms. Classical algorithms struggle to create accurate, complex, and prompt financial analytical models resulting in over 25% of small and medium sized financial institutions losing customers each year. This study explores whether a hybrid quantum neural network (QCNN) design provides a quantum advantage in either accuracy or time in the fin tech task of credit defaulting. The hybrid quantum neural network model uses a variational quantum circuit built on a continuous variable architecture. The data flow of the network consists of a classical network, quantum data encoding (which uses quantum squeezers, interferometers, displacement, and Kerr gates), QCNN, and measurement. Although the quantum model took a longer time to train, it reported a higher prediction accuracy of 97.63% compared to 91.20% for the classical network. This work demonstrates the future implications of quantum integration in the financial field and the quantum advantage it offers on classification tasks.
Quantum machine learning has been shown to offer exponential computational boosts compared to current classical models through the exploitation of quantum mechanical properties such as entanglement and superposition. The finance sub field of fin-tech, where financial services are constructed around a customer's demands, is believed to benefit with the use of hybrid quantum-classical machine learning algorithms. Classical algorithms struggle to create accurate, complex, and prompt financial analytical models resulting in over 25% of small and medium sized financial institutions losing customers each year. This study explores whether a hybrid quantum neural network (QCNN) design provides a quantum advantage in either accuracy or time in the fin tech task of credit defaulting. The hybrid quantum neural network model uses a variational quantum circuit built on a continuous variable architecture. The data flow of the network consists of a classical network, quantum data encoding (which uses quantum squeezers, interferometers, displacement, and Kerr gates), QCNN, and measurement. Although the quantum model took a longer time to train, it reported a higher prediction accuracy of 97.63% compared to 91.20% for the classical network. This work demonstrates the future implications of quantum integration in the financial field and the quantum advantage it offers on classification tasks.
In modern financial information systems, the growing complexity of credit risk assessment for small and medium-sized enterprises (SMEs) creates increasing demands for highly accurate, scalable, and computationally efficient predictive models.Traditional credit scoring approaches based on classical statistical methods and conventional neural networks often demonstrate limited performance when processing nonlinear financial dependencies, noisy datasets, and highdimensional feature spaces.These limitations become especially significant under restricted training conditions, where the amount of labeled financial data is limited and rapid model convergence is required for practical deployment in banking and risk management environments.In such conditions, hybrid quantum-classical machine learning has emerged as a promising direction that combines the representational capabilities of classical neural networks with the computational potential of quantum variational circuits.This paper proposes a hybrid quantum-classical neural network architecture designed for SME credit risk assessment under limited training conditions.The proposed model integrates classical dense layers with a variational quantum circuit that performs nonlinear feature transformation through quantum embedding, parameterized quantum gates, and entanglement operations.Particular attention is devoted to efficient data encoding and optimization of variational circuit depth in order to reduce training complexity while preserving predictive stability.
This study investigates the integration of quantum circuits with classical neural networks for enhancing credit scoring for small- and medium-sized enterprises (SMEs). We introduce a hybrid quantum–classical model, focusing on the synergy between quantum and classical rather than comparing the performance of separate quantum and classical models. Our model incorporates a quantum layer into a traditional neural network, achieving notable reductions in training time. We apply this innovative framework to a binary classification task with a proprietary real-world classical credit default dataset for SMEs in Singapore. The results indicate that our hybrid model achieves efficient training, requiring significantly fewer epochs (350) compared to its classical counterpart (3500) for a similar predictive accuracy. However, we observed a decrease in performance when expanding the model beyond 12 qubits or when adding additional quantum classifier blocks. This paper also considers practical challenges faced when deploying such models on quantum simulators and actual quantum computers. Overall, our quantum–classical hybrid model for credit scoring reveals its potential in industry, despite encountering certain scalability limitations and practical challenges.
The growing complexity, interdependencies, and rapid fluctuations inherent in modern financial markets create substantial challenges for accurate forecasting, portfolio optimization, and risk management. Conventional machine learning techniques, while powerful, often face limitations in capturing nonlinear relationships and processing high-dimensional datasets efficiently. Quantum machine learning (QML) has emerged as a promising paradigm that leverages quantum computing principles to enhance predictive modeling in finance. This study presents a comprehensive investigation into the application of QML methods—including variational quantum circuits, quantum kernel estimation, and quantum-enhanced support vector machines—for financial time-series prediction and asset price classification. We propose a hybrid quantum-classical framework that integrates quantum feature mapping with classical optimizers to improve model expressiveness and convergence. Empirical experiments are conducted using historical stock market data and synthetic datasets to benchmark QML approaches against established classical models such as long short-term memory networks and gradient boosting machines. The results demonstrate that QML techniques can achieve superior prediction accuracy and lower computational latency under certain data regimes, particularly when dealing with small-to-medium-sized datasets and high feature correlations. Additionally, the study examines scalability considerations, hardware constraints of near-term quantum devices, and the interpretability of quantum model outputs in financial decision-making contexts. The findings underscore the transformative potential of quantum machine learning as an innovative tool for advancing predictive analytics in finance and provide practical insights into how financial institutions can begin integrating QML capabilities into their workflows.
Accurate prediction of future loan defaults is a critical capability for financial institutions that provide lines of credit. For institutions that issue and manage extensive loan volumes, even a slight improvement in default prediction precision can significantly enhance financial stability and regulatory adherence, resulting in better customer experience and satisfaction. Datasets associated with credit default prediction often exhibit temporal correlations and high dimensionality. These attributes can lead to accuracy degradation and performance issues when scaling classical predictive algorithms tailored for these datasets. Given these limitations, quantum algorithms, leveraging their innate ability to handle high-dimensionality problems, emerge as a promising new avenue alongside classical approaches. To assess the viability and effectiveness of quantum methodologies, we investigate a hybrid quantum-classical algorithm, utilizing a publicly available "Default Prediction Dataset" released as part of a third-party data science competition. Specifically, we employ hybrid quantum-classical machine learning models based on projected quantum feature maps and their ensemble integration with classical models to examine the problem of credit card default prediction. Our results indicate that the ensemble models based on the projected quantum features were capable of slightly improving the purely classical results expressed via a "Composite Default Risk" (CDR) metric. Furthermore, we discuss the practical applicability of the studied quantum-classical machine learning techniques and address open questions concerning their implementation.
In the financial sector, machine learning (ML) has emerged as a transformative technology that provides sophisticated solutions to critical challenges, such as in fraud detection, anti-money laundering, algorithmic trading, credit scoring, and financial forecasting. ML significantly improves the accuracy, speed, and efficacy of decision-making processes by using sophisticated algorithms and large datasets. Nevertheless, its integration into financial systems presents significant obstacles such as cybersecurity threats, regulatory compliance, and data quality concerns. Building on these developments, this review examines the emergence of quantum machine learning (QML), an approach that integrates quantum-computation principles with intelligent financial systems. QML exploits quantum phenomena, including superposition, entanglement, and parallelism, to overcome the computational constraints encountered in classical ML. Applications such as quantum support vector machines (QSVMs), quantum neural networks (QNNs), and quantum clustering are examined in the context of fraud detection, portfolio optimization, and market forecasting. In contrast to previous reviews, this study provides a comparative and integrative analysis of ML and QML in the financial domain. It highlights the current technical limitations and emergent hybrid quantum classical models, emphasizing strategic directions for future research. In addition, we suggest a new framework for assessing the suitability of QML in practical financial applications, thereby bridging the gap between theoretical potential and real-world deployment. This review not only consolidates recent advancements but also establishes the foundation for the next iteration of intelligent financial systems powered by quantum-enhanced technologies. • Quantum Machine Learning (QML) leverages unique quantum properties like superposition, entanglement, and parallelism to overcome the computational limitations of traditional ML approaches. • Techniques such as Quantum Support Vector Machines (QSVM), Quantum Neural Networks (QNN), and quantum clustering demonstrate significant advancements in efficiency and accuracy for financial applications. • QML showcases transformative potential in solving complex financial problems, including fraud detection, portfolio optimization, and market forecasting, with greater accuracy and computational efficiency. • The potential for hybrid quantum-classical models to stabilize hardware and advance quantum algorithms offers a pathway to overcome current limitations, paving the way for scalable and robust financial systems.
In recent years, the field of machine learning has witnessed a paradigm shift with the emergence of hybrid quantum-classical neural networks. These networks combine the power of classical neural networks with the computational advantages offered by quantum variational circuits. This study presents a comprehensive survey of hybrid quantum-classical machine learning algorithms, focusing on their architecture, performance, and hyperparameter optimization techniques. Introducing the concept of hybrid quantum-classical machine learning and its significance in contemporary image classification tasks. It explores the theoretical foundations of quantum variational circuits and their integration with classical neural network architectures. Special attention is given to prominent algorithms such as Quantum Convolutional Neural Networks, Quanvolutional Neural Networks, and Quantum ResNet, each offering unique advantages in image classification tasks. A major aspect covered in this study is the hyperparameter optimization techniques employed in hybrid quantum-classical machine learning. The chapter discusses quantum-inspired optimization methods tailored for efficiently tuning the hyperparameters of complex neural network models. Through comparative analysis, the effectiveness of these optimization techniques is demonstrated in reducing computational overhead and improving model performance. Moreover, the study also delves into experimental results obtained from benchmarking hybrid quantum-classical models against traditional classical approaches. It highlights the accuracy variations observed across different quantum architectures and explores the impact of architectural permutations on model convergence. Insights gained from these experiments provide valuable guidance for researchers and practitioners seeking to leverage hybrid quantum-classical neural networks in real-world applications.