Topic: When does reinforcement learning stand out in quantum control?
Abstract: A comparative study on state preparation. Reinforcement learning has been widely used in many problems, including quantum control of qubits. However, such problems can, at the same time, be solved by traditional, non-machine-learning methods, such as Krotov algorithms, and it remains unclear which one is most suitable when the control has specific constraints. In this regard, I will talk about the comparative performance of three reinforcement learning algorithms: tabular Q-learning, deep Q-learning, and policy gradient, and two non-reinforcement learning algorithms: stochastic gradient descent and Krotov algorithms, in the problem of preparing a desired quantum state.
Bio: Asad is a mathematics undergraduate student at the City University of Hong Kong, with minors in physics and computer science. Asad has been working on the intersection of quantum information and machine learning throughout his undergraduate. He has also worked on quantum machine learning with Entropica Labs in Singapore and with Google Summer of Code. Currently, he is finishing his thesis with Prof. Oscar Dahlsten on quantum machine learning at SUSTech and CityU.
Moderators : Xiao Ming Zhang is a PhD student in department of physics, City University of Hong Kong, advised by Xin Wang. His research interests are in quantum information, quantum machine learning and quantum optimal control. He has worked on the application of reinforcement learning to quantum optimal control problems, including quantum state preparation, quantum state transfer, etc. He also works on the quantum machine learning algorithms using near-term quantum computers, and potential of realizing quantum advantage.
Professor Terrill Frantz of Harrisburg University, https://quantumapalooza.com/