Work

Applications of Quantum Machine Learning for Quantitative Finance

Year: 2024

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2405.10119

Authors: Mironowicz, Piotr, H., Akshata Shenoy, Mandarino, Antonio, Yilmaz, A. Ege, Ankenbrand, Thomas

Institution: Lucerne University of Applied Sciences and Arts

Language: English

Topic: Quantum Computing Algorithms and Architecture

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: Fundacja na rzecz Nauki Polskiej, Knut och Alice Wallenbergs Stiftelse, QuantERA, Narodowe Centrum Badań i Rozwoju, Wallenberg Centre for Molecular and Translational Medicine, Wallenberg Center for Quantum Technology, Chalmers University of Technology

Awards: Implementation-oriented Device Independent CrypTography, G2216297582, G5541277979, G6182770146, G7569305816

OpenAlex ID: W4397028495

Cites:

Cited by: 7

Related to: 10