Applications of Quantum Machine Learning for Quantitative Finance

• URL: http://arxiv.org/abs/2405.10119v1
• Date: Thu, 16 May 2024 14:15:44 GMT
• Title: Applications of Quantum Machine Learning for Quantitative Finance
• Authors: Piotr Mironowicz, Akshata Shenoy H., Antonio Mandarino, A. Ege Yilmaz, Thomas Ankenbrand,
• Abstract summary: Machine learning and quantum machine learning (QML) have gained significant importance, as they offer powerful tools for tackling complex computational problems. This work gives an extensive overview of the uses of QML 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Quantum Machine Learning: An Interplay Between Quantum Computing and Machine Learning [54.80832749095356]
  Quantum machine learning (QML) is a rapidly growing field that combines quantum computing principles with traditional machine learning. This paper introduces quantum computing for the machine learning paradigm, where variational quantum circuits are used to develop QML architectures.
  arXiv  Detail & Related papers  (2024-11-14T12:27:50Z)
• Leveraging Pre-Trained Neural Networks to Enhance Machine Learning with Variational Quantum Circuits [48.33631905972908]
  We introduce an innovative approach that utilizes pre-trained neural networks to enhance Variational Quantum Circuits (VQC) This technique effectively separates approximation error from qubit count and removes the need for restrictive conditions. Our results extend to applications such as human genome analysis, demonstrating the broad applicability of our approach.
  arXiv  Detail & Related papers  (2024-11-13T12:03:39Z)
• A Brief Review of Quantum Machine Learning for Financial Services [0.0]
  This review paper examines state-of-the-art algorithms and techniques in quantum machine learning with potential applications in finance. The financial applications considered include risk management, credit scoring, fraud detection, and stock price prediction.
  arXiv  Detail & Related papers  (2024-07-17T14:44:47Z)
• A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges [60.546677053091685]
  Large language models (LLMs) have unlocked novel opportunities for machine learning applications in the financial domain. We explore the application of LLMs on various financial tasks, focusing on their potential to transform traditional practices and drive innovation. We highlight this survey for categorizing the existing literature into key application areas, including linguistic tasks, sentiment analysis, financial time series, financial reasoning, agent-based modeling, and other applications.
  arXiv  Detail & Related papers  (2024-06-15T16:11:35Z)
• Quantum Computing Enhanced Service Ecosystem for Simulation in Manufacturing [56.61654656648898]
  We propose a framework for a quantum computing-enhanced service ecosystem for simulation in manufacturing. We analyse two high-value use cases with the aim of a quantitative evaluation of these new computing paradigms for industrially-relevant settings.
  arXiv  Detail & Related papers  (2024-01-19T11:04:14Z)
• Quantum Finance: a tutorial on quantum computing applied to the financial market [0.7388859384645263]
  This article focuses on the fundamentals of quantum computing, focusing on a promising quantum algorithm and its application to a financial market problem. We not only describe the main concepts involved but also consider simple practical examples involving financial assets available on the Brazilian stock exchange, with codes, both classic and quantum, freely available as a Jupyter Notebook.
  arXiv  Detail & Related papers  (2022-08-08T19:37:27Z)
• A Survey of Quantum Computing for Finance [15.341098545888944]
  Finance is estimated to be the first industry sector to benefit from quantum computing. Quantum computers are expected to surpass the computational capabilities of classical computers during this decade.
  arXiv  Detail & Related papers  (2022-01-08T06:16:21Z)
• Quantum Machine Learning for Finance [52.97198108304122]
  Finance is estimated to be the first industry sector to benefit from Quantum Computing. This review paper presents the state of the art of quantum algorithms for financial applications.
  arXiv  Detail & Related papers  (2021-09-09T14:20:10Z)
• Understanding the Usability Challenges of Machine Learning In High-Stakes Decision Making [67.72855777115772]
  Machine learning (ML) is being applied to a diverse and ever-growing set of domains. In many cases, domain experts -- who often have no expertise in ML or data science -- are asked to use ML predictions to make high-stakes decisions. We investigate the ML usability challenges present in the domain of child welfare screening through a series of collaborations with child welfare screeners.
  arXiv  Detail & Related papers  (2021-03-02T22:50:45Z)
• Quantum Computing for Finance: State of the Art and Future Prospects [8.77758485723332]
  This article outlines our point of view regarding the applicability, state-of-the-art, and potential of quantum computing for problems in finance. We describe in detail quantum algorithms for specific applications arising in financial services, such as those involving simulation, optimization, and machine learning problems. In addition, we include demonstrations of quantum algorithms on IBM Quantum back-ends and discuss the potential benefits of quantum algorithms for problems in financial services.
  arXiv  Detail & Related papers  (2020-06-25T16:02:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.