Work

Quantum machine learning for quantitative finance

Year: 2026

Type: article

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.

Source: Quantum Science and Technology

DOI: 10.1088/2058-9565/aeb8ed

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

Institutions: Gdańsk University of Technology, University of Gdańsk, 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: hybrid

Estimated APC paid: $3,895