arXiv
Open Access
2024
Applications of Quantum Machine Learning for Quantitative Finance
Piotr Mironowicz
Akshata Shenoy H.
Antonio Mandarino
A. Ege Yilmaz
Thomas Ankenbrand
Abstrak
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.
Topik & Kata Kunci
Penulis (5)
P
Piotr Mironowicz
A
Akshata Shenoy H.
A
Antonio Mandarino
A
A. Ege Yilmaz
T
Thomas Ankenbrand
Akses Cepat
Informasi Jurnal
- Tahun Terbit
- 2024
- Bahasa
- en
- Sumber Database
- arXiv
- Akses
- Open Access ✓