Quantum Gradient Descent Algorithm


Title: Quantum Gradient Descent Algorithm

Speaker: Zhang Guofeng(The Hong Kong Polytechnic University)

Time: 11am-12am,27th June,2018

Location: Communication Building 818 Conference Room, Shahe Campus, UESTC.


张国峰.jpg

Abstract:

The gradient descent algorithm is an iterated optimization algorithm which finds a local minimum of a function by searching along the steepest descent direction of the function.  The gradient descent algorithm is a simple optimization method which has wide applications --- a famous example being the backpropagation algorithm in the training of neural networks. In this talk, we discuss a quantum version of the gradient descent algorithm. We first touch on the fundamentals of quantum mechanics, such as states, system variables, composite systems, partial trace and quantum measurement, then we discuss quantum Fourier transform which is key to quantum phase estimation; finally, we present the quantum gradient descent algorithm.


Profile:

Guofeng Zhang received his B.Sc. degree and M.Sc. degree from Northeastern University, Shenyang, China, in 1998 and 2000 respectively. He received a Ph.D. degree in Applied Mathematics from the University of Alberta, Edmonton, Canada, in 2005. During 2005–2006, he was a Postdoc Fellow at the University of Windsor, Windsor, Canada. He joined the School of Electronic Engineering of the University of Electronic Science and Technology of China, Chengdu, China, in 2007. From April 2010 to December 2011 he was a Research Fellow at the Australian National University. He is currently an Associate Professor in the Department of Applied Mathematics at the Hong Kong polytechnic University. His research interests include quantum information and control, sampled-data control and nonlinear dynamics.




 

0.0634s