The Master of Science in Data-Driven Modeling, a collaborative initiative between the Department of Physics and the Department of Mathematics at the Hong Kong University of Science and Technology, mandates the completion of a supervised research project for all enrolled students. These projects serve as a capstone to the students’ academic and practical learning experiences. Below is a catalog of projects that I have had the privilege to supervise or co-supervise.
2023 Fall - 2024 Spring
Project Title: The Boundary Condition of Quantum-Inspired Sampling Techniques: A Systematic Review
- Students: Zhenxing Tan (Ongoing), Jialiang Xu (Ongoing)
2022 Fall - 2023 Spring
Project Title: Reinforcement Learning for Continuous Control: A Quantum Normalized Advantage Function Approach
- Students: Yaofu Liu (Graduated), Chang Xu (Graduated)
- Outcome: QSW 2023, Code; Y. Liu received the Best Presentation Award of DDM 6980 Project Presentation.
- Abstract: In this study, we present a new approach to quantum reinforcement learning that can handle tasks with a range of continuous actions. Our method uses a quantum version of the classic normalized advantage function (QNAF), only needing the Q-value network created by a quantum neural network and avoiding any policy network. We implemented the method by TensorFlow framework. When tested against standard Gym benchmarks, QNAF outperforms classical NAF and prior quantum methods in terms of fewer adjustable parameters. Furthermore, it shows improved stability, reliably converging regardless of changes in initial random parameters.
- Quantum Finance Research