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.
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.
Abstract Machine learning (ML) and quantum machine learning (QML) have gained significant importance, as they offer powerful tools for tackling complex computational problems across various domains. An overview of QML's uses in quantitative finance is presented here. The connection between quantum computing and machine learning in financial applications, spanning a range of use cases including portfolio optimization, market prediction and trading, pricing and risk management is examined. The different applications from the literature review are categorized based on specific use cases, ML approaches, optimization methods, and the types of quantum hardware utilized. To facilitate this examination, we employ a morphological box, which allows us to analyze different combinations of these elements across financial use cases.
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.
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.
Quantum computing has the potential to revolutionize many industries, including banking. It is expected that by the end of this decade, quantum computers will have surpassed regular computers in processing capabilities, which will have far-reaching consequences for many sectors, including the financial sector. In particular, it is predicted that the financial industry will gain the most in the short, medium, and long term from using quantum computing. The authors of this chapter discuss the potential benefits of combining machine learning with quantum computing for use in financial modeling. Combining quantum computing and machine learning, or quantum machine learning (QML), provides novel approaches to solving difficult economic problems that classical computers are ill-equipped to tackle. The benefits of employing QML for risk management, credit rating, portfolio optimization, option pricing, and uncovering arbitrage opportunities are addressed, as well as an introduction to the principles of quantum computing. It also describes the quantum hardware and software and the potential challenges and ethical problems associated with QML’s use in the financial sector.
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.