Expressibility-Enhancing Strategies for Quantum Neural Networks (Slides)

Quantum computing, whether it is quantum computer hardware or quantum algorithms, has been developing rapidly recently. It has the potential to solve challenging scientific and engineering problems that cannot be efficiently solved by classical computers. Deep learning has become very powerful in many important areas, but it can involve an extremely large number of parameters, making the training very challenging. Quantum machine learning, especially quantum neural networks, has the potential to solve this problem, although it is still in its infancy. The speaker will first briefly introduce the basic concepts of quantum computing, and then discuss our recent work on strategies to enhance the expressibility of quantum neural networks. These strategies could make our quantum neural network more suitable for power system applications. Time permitting, the speaker will also briefly describe his recent work on developing quantum algorithms for NP-complete problems, an extremely important but unsolved problem in computer science and mathematics.

Who would benefit from this webinar:
Undergraduate and graduate students, researchers, engineers