Quantum machine learning for quantitative finance

Piotr Mironowicz, Akshata Shenoy, Antonio Mandarino, A. E. Yilmaz, Thomas Ankenbrand

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: semanticscholar · PDF

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