Modern quantum machine learning algorithms and techniques are reviewed in this review paper with possible financial applications. Along with quantum generative AI techniques like Quantum Transformers and Quantum Graph Neural Networks (QGNNs), we discuss QML techniques in supervised learning tasks like Quantum Variational Classifiers, Quantum Kernel Estimation, and Quantum Neural Networks (QNNs). Risk management, credit scoring, fraud detection, and stock price prediction are among the financial applications that are taken into consideration. Additionally, we offer a summary of QML's drawbacks, possibilities, and restrictions in these particular domains as well as more widely throughout the field. With the help of this, we hope to provide data scientists, financial industry professionals, and enthusiasts with a quick overview of why quantum computing, and QML in particular, might be worthwhile to investigate in their respective fields of expertise.
Modern quantum machine learning algorithms and techniques are reviewed in this review paper with possible financial applications. Along with quantum generative AI techniques like Quantum Transformers and Quantum Graph Neural Networks (QGNNs), we discuss QML techniques in supervised learning tasks like Quantum Variational Classifiers, Quantum Kernel Estimation, and Quantum Neural Networks (QNNs). Risk management, credit scoring, fraud detection, and stock price prediction are among the financial applications that are taken into consideration. Additionally, we offer a summary of QML's drawbacks, possibilities, and restrictions in these particular domains as well as more widely throughout the field. With the help of this, we hope to provide data scientists, financial industry professionals, and enthusiasts with a quick overview of why quantum computing, and QML in particular, might be worthwhile to investigate in their respective fields of expertise.
Modern quantum machine learning algorithms and techniques are reviewed in this review paper with possible financial applications. Along with quantum generative AI techniques like Quantum Transformers and Quantum Graph Neural Networks (QGNNs), we discuss QML techniques in supervised learning tasks like Quantum Variational Classifiers, Quantum Kernel Estimation, and Quantum Neural Networks (QNNs). Risk management, credit scoring, fraud detection, and stock price prediction are among the financial applications that are taken into consideration. Additionally, we offer a summary of QML's drawbacks, possibilities, and restrictions in these particular domains as well as more widely throughout the field. With the help of this, we hope to provide data scientists, financial industry professionals, and enthusiasts with a quick overview of why quantum computing, and QML in particular, might be worthwhile to investigate in their respective fields of expertise.
Abstract This review paper examines state-of-the-art algorithms and techniques in quantum machine learning (QML) with potential applications in finance. We discuss QML techniques in supervised learning tasks, such as quantum variational classifiers, quantum Kernel estimation, and quantum neural networks, along with quantum generative AI techniques like quantum transformers and quantum graph neural networks. The financial applications considered include risk management, credit scoring, fraud detection, and stock price prediction. We also provide an overview of the challenges, potential, and limitations of QML, both in these specific areas and more broadly across the field. We hope that this can serve as a quick guide for data scientists, professionals in the financial sector, and enthusiasts in this area to understand why quantum computing and QML in particular could be interesting to explore in their field of expertise.
Machine learning and quantum machine learning (QML) have gained significant importance, as they offer powerful tools for tackling complex computational problems across various domains. This work gives an extensive overview of QML uses in quantitative finance, an important discipline in the financial industry. We examine the connection between quantum computing and machine learning in financial applications, spanning a range of use cases including fraud detection, underwriting, Value at Risk, stock market prediction, portfolio optimization, and option pricing by overviewing the corpus of literature concerning various financial subdomains.
The synergy between quantum computing and machine learning gives future potential in the financial sector. The unique features such as superposition and entanglement in quantum computing are utilized in Quantum Machine Learning (QML) to solve the challenges that dominate the modern era of finance. The subject of this paper explains the use of Quantum Machine Learning (QML) in finance including risk management, portfolio optimization, market trend forecasting, and fraud detection. In portfolio optimization, the aim is to reduce risk at the same time maximizing return, the quantum-enhanced methodology does it better than the classical method. QML is applied for risk analysis because it efficiently computes values such as Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR). In finance, improved sentiment analysis, anomaly detection to prevent fraud, and prediction of market trends are enabled with QML algorithms. QML improves credit rating, financial time series forecasting, and derivative pricing with its ability to process large amounts of data.
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.
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.