Treffer: D5.12: Evaluation of quantum algorithms for finance

This report presents the advancements in quantum computing techniques for quantitative finance within Use Case 5 (UC5) of the NEASQC project. The research is a collaborative effort among the University of A Coruna (UDC), the Galician Supercomputing Center (CESGA), and the Hong Kong and Shanghai Banking Corporation (HSBC). Each organization brings unique expertise to the project: UDC focuses on theoretical foundations, CESGA on technological implementation, and HSBC on identifying industry-relevant problems. The research addresses two main areas critical to HSBC: Quantum Accelerated Monte Carlo (QAMC) for option pricing and Quantum Machine Learning (QML) for risk assessment using metrics such as Value at Risk (VaR). These areas are essential for enhancing the efficiency and accuracy of financial modeling and risk management. More specifically, QAMC aims to expedite the option pricing process, while QML provides advanced techniques for risk assessment, offering deeper insights and more robust predictions. Despite significant progress, the integration of these quantum techniques into industrial applications remains challenging due to current hardware limitations and the early stage of quantum computing technology. The project highlights the need for further development of quantum hardware and algorithms to bridge the technological gap between classiccal and quantum methods.Notable advancements include new techniques for QAMC and QML, which are particularly relevant for financial institutions. The project has also produced five research papers and developed a comprehensive software library, QQuantLib, to facilitate further research and experimentation. In conclusion, while significant progress has been made in developing quantum algorithms for pricing and VaR estismation, these algorithms are not yet enough competitive with classical algorithms on current Noisy Intermediate-Scale Quantum (NISQ) architectures. The primary challenges for moving forward are related to the execution of QAMC and QML algorithms, which ...