[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122387-en":3,"doc-seo-122387-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122387,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","A Brief Review of Quantum Machine Learning for Financial Services","Review of state-of-the-art quantum machine learning (QML) methods for financial services, focusing on supervised techniques such as Quantum Variational Classifiers, Quantum Kernel Estimation, and Quantum Neural Networks, plus generative approaches including Quantum Transformers and Quantum Graph Neural Networks. It surveys finance use cases in risk management, credit scoring, fraud detection, and stock price prediction, and outlines key challenges, promises, and limitations. The paper targets data scientists, finance professionals, and practitioners seeking a concise, practical entry point to QML adoption.","arXiv :2407 . 12618v1 [ quant-ph] 17 Jul 2024  \nA Brief Review of Quantum Machine Learning for Financial Services  \nMina Doosti∗,1, Petros Wallden1 , Conor Brian Hamill2 , Robert Hankache2 , Oliver  \nThomson Brown3 , and Chris Heunen1  \n1 School of Informatics, Quantum Software Lab, University of Edinburgh, United Kingdom  \n2 Data Science & Innovation, NatWest Group, London, United Kingdom  \n3 EPCC, Quantum Software Lab, University of Edinburgh, United Kingdom  \nAbstract  \nThis review paper examines state-of-the-art algorithms and techniques in quantum machine learning 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 (QNNs), along with quantum generative AI techniques like Quantum Transformers and Quantum Graph Neural Networks (QGNNs) . 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.  \n1 Introduction  \nQuantum computing, as a revolutionary and fundamentally different paradigm in computation, has significantly impacted diverse domains, including machine learning and data science. As machine learning continues to spearhead technological advancements in data science, the convergence of quantum computing and machine learning has garnered considerable interest, with the aspiration of transcending the boundaries of classical machine learning methodologies. The inception of Quantum Machine Learning (QML) dates back to the mid-1990s, initially exploring the concepts of quantum learning theory [1] . However, it wasn’t until approximately 18 years ago that the field gained significant attention following the seminal paper by Harrow, Hassidim, and Lloyd [2] . This pivotal work laid the foundation for numerous studies in both supervised and unsupervised learning domains [3, 4, 5, 6, 7, 8, 9, 10] . Leveraging non-classical properties such as entanglement, superposition, and interference, quantum algorithms offer the potential for substantial speedups, sometimes exponential, over classical computing counterparts. While such speedups have been demonstrated for certain specific problems, achieving them in the realm of data science, and moreover for industry applications, remains an ongoing challenge with much active research in QML attempting to address it.  \nIn this brief and concise review, instead of a broad examination of the field of quantum machine learning (as done in [11, 12, 13]), we shift our focus to techniques and algorithms that  \n∗ [mdoosti@ed.ac.uk](mdoosti@ed.ac.uk)  \nparallel classical methods utilized in finance. We aim to highlight the potentials, promises, and challenges of applying QML within the financial sector. This review can also serve as a guide for financial professionals, both data scientists and management, offering an understanding of the latest advancements in QML relevant to their sector. Doing so, this review can aid decision-making processes, enhance the adoption of cutting-edge technologies, and lead to a deeper and hype-free appreciation of how QML can improve financial services.  \nBroadly categorized, QML encompasses three areas based on the nature of the algorithm and data: quantum algorithms on classical data, classical algorithms on quantum data and quantum algorithms on quantum data. Our review is centred on the first category, where classical data interfaces with quantum algorithms, as it aligns with the applications in finance, such as credit scoring, risk management, stock price prediction, and fraud detection. W","cbCaiaWP7vNaFYUE","https://ap.wps.com/l/cbCaiaWP7vNaFYUE","pdf",212463,1,19,"English","en",105,"# Introduction\n## Applications of Classical Machine Learning in Finance\n## Credit Scoring\n## Risk Management\n## Fraud Detection\n## Stock Price Prediction\n## QML Techniques for Supervised Learning\n## Quantum Generative AI for Finance\n## Challenges, Promises, and Limitations","[{\"question\":\"Which quantum machine learning techniques are covered for supervised learning in finance?\",\"answer\":\"The review discusses Quantum Variational Classifiers, Quantum Kernel Estimation, and Quantum Neural Networks (QNNs) as supervised-learning approaches for financial tasks.\"},{\"question\":\"What finance problem areas does the review consider?\",\"answer\":\"It addresses risk management, credit scoring, fraud detection, and stock price prediction, linking each to relevant QML potential.\"},{\"question\":\"What does the paper say about the challenges and limitations of QML?\",\"answer\":\"Beyond potential speedups, it emphasizes that achieving reliable advantages for data-science and real industry applications remains an ongoing challenge, and it outlines broader limitations across the field.\"}]","A Brief Review of Quantum Machine Learning for Financial Services | 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quantum machine learning techniques are covered for supervised learning in finance?","Question",{"text":75,"@type":76},"The review discusses Quantum Variational Classifiers, Quantum Kernel Estimation, and Quantum Neural Networks (QNNs) as supervised-learning approaches for financial tasks.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What finance problem areas does the review consider?",{"text":80,"@type":76},"It addresses risk management, credit scoring, fraud detection, and stock price prediction, linking each to relevant QML potential.",{"name":82,"@type":73,"acceptedAnswer":83},"What does the paper say about the challenges and limitations of QML?",{"text":84,"@type":76},"Beyond potential speedups, it emphasizes that achieving reliable advantages for data-science and real industry applications remains an ongoing challenge, and it outlines broader limitations across the 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