My primary area of research focuses on the application of quantum computing within the financial sector. Currently, I am engaged in several initiatives in collaboration with a local bank, UOB, and a stock exchange, SGX, concentrating on quantum Monte Carlo methods and optimisation strategies for market risk assessment and enhancing the transaction settlement process. These projects are yielding promising outcomes. Before this, I successfully completed two research endeavors in partnership with TradeTeq and Oneconnect, exploring the potential benefits of quantum machine learning (QML) for evaluating credit risk in trade finance for SMEs and in decentralised consensus protocols. Additionally, my work with an underwriter on optimising supply chain financing portfolios has produced notable insights. I have also contributed to research aimed at enhancing the visualisation of quantum states within circuits and quantum neural networks.
Website: https://sites.google.com/view/prgqc/home
Selected publications:
- Exponential Qubit Reduction in Optimization for Financial Transaction Settlement https://arxiv.org/abs/2307.07193
- Review of some existing QML frameworks and novel hybrid classical–quantum neural networks realising binary classification for the noisy datasets, Sci. Rep. 12, 1, (2022)
- Quantum Consensus, 2019 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE): Proceedings, 1-8 (2019)
- Quantum Computing for Supply Chain Finance, 2021 IEEE International Conference on Services Computing (SCC), 456-459 (2021)
- QuantumEyes: Towards Better Interpretability of Quantum Circuits. IEEE Transactions on Visualization and Computer Graphics doi: 10.1109/TVCG.2023.3332999 (2023)
