All members of the HKU community and the general public are welcome to join!
Speaker: Mr Yan Zhu, PhD Candidate, Department of Computer Science, Faculty of Engineering, HKU
Moderator: Mr Liu Qiushi, PhD Candidate, Department of Computer Science, Faculty of Engineering, HKU
Date: 28th March 2023 (Tuesday)
Mode: Mixed (both face-to-face and online). Seats for on-site participants are limited. A confirmation email will be sent to participants who have successfully registered.
Deep neural networks are a powerful tool for the characterization of quantum states. Existing networks are typically trained with experimental data gathered from the specific quantum state that needs to be characterized. In this talk, Mr. Yan Zhu, from Department of Computer Science, will introduce a model of network that can be trained with classically simulated data from a fiducial set of states and measurements, and can later be used to characterize quantum states that share structural similarities with the states in the fiducial set. With little guidance of quantum physics, the network builds its own data-driven representation of quantum states, and then uses it to predict the outcome statistics of quantum measurements that have not been performed yet. The state representation produced by the network can also be used for tasks beyond the prediction of outcome statistics, including clustering of quantum states and identification of different phases of matter.