PHYSICS DISSERTATION DEFENSE: Shangnan Zhou
Ph.D. Candidate: Shangnan Zhou
Research Co-Advisors: Patrick Hayden, Stephen Shenker, Leonard Susskind
Date: November 23rd, 2022
Time: 2 PM
Zoom Link: https://stanford.zoom.us/j/93731284622
Zoom Password: Email nickswan [at] stanford.edu (nickswan[at]stanford[dot]edu) for password
Title: New Era of Quantum Machine Learning
Abstract: Quantum computing technology takes advantage of the unique properties of quantum states to perform calculations. Quantum machine learning integrates quantum technology to machine learning, which promises to tackle persistent challenges such as the lack of labeled training data, and the limit of computational power. Quantum machine learning is also naturally better at recognizing underlying quantum data patterns. As the world is intrinsically quantum, this sheds lights on many fields of science and engineering.
In this talk, I will present two milestones from my recent works. First, I showcase a general protocol for systematically designing machine learning algorithms that provide quantum speed-ups for classical learning tasks. Second, I propose a new quantum information quantity, the quantum cross entropy. I will exhibit its significance in quantum data compression, quantum communication, and quantum machine learning. I will conclude with my long-term vision for quantum machine learning.