Quantum Machine Learning for Financial Applications

• Fernando Alonso ; Álvaro Leitao ^[1] ; Carlos Vázquez ^[1]
  1. [1] Universidade da Coruña

     Universidade da Coruña

     A Coruña, España

     
• Localización: Proceedings XoveTIC 2025: Impulsando el talento científico / coord. por Manuel Lagos Rodríguez, Hilda Romero-Velo, Álvaro Leitao , Javier Pereira-Loureiro , Manuel Penedo , 2026, ISBN 978-84-9749-925-5, págs. 67-74
• Idioma: inglés
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• Resumen

  • The rise of quantum technologies has led to growing interest in exploring the useof quantum circuits alongside ML models—also referred to as Quantum Machine Learning(QML)—to enhance the development of new applications, such as through Parametrized Quan-tum Circuits (PQCs). Among the applications enabled by the use of PQCs in the financial world,some of the most relevant include the computation of risk metrics, as well as the option pricing offinancial derivatives. This work aims to illustrate the study of the aforementioned applications,where the main idea consists in approximating, through the PQC, the underlying distribution ofthe assets and the payoff function, and from there, computing risk metrics and derivative prices.