Presenter

Yudong Cao is the CTO of Zapata Computing - a Harvard spinout quantum computing software and algorithm company funded by The Engine, the venture firm founded by MIT to invest in tough tech. Dr. Cao has a background in Mechanical Engineering and Computer Science. From 2016-2018 he held the position as a postdoctoral researcher at Harvard University working closely with Professor Alán Aspuru-Guzik, a world leader in quantum simulation for chemistry and materials and co-founder of Zapata Computing. The main focus of Dr. Cao's work at Harvard was on developing and deploying algorithms for noisy intermediate-scale quantum devices. This work has served as the foundation for the applications and solutions Zapata can offer their enterprise clients today.

In this talk, Dr. Cao will present two different lines of work that have been pursued by the Zapata team - one using quantum annealers (manufactured by D-Wave) and the other using gate-model quantum devices. The goal is both accelerating the training process and also enhancing the representative power of the hybrid quantum-classical model. Dr. Cao will also use concrete examples to talk about how such quantum-enhanced technique can be integrated with existing neural network architectures.