Yudong Cao

Graduate Student

Joined department: Fall 2013
Education
PhD, Purdue University, Computer Science (2016)
MS, Purdue University, Computer Science (2015)
MS, Purdue University, Mechanical Engineering (2013)
BS, Purdue University, Mechanical Engineering (2011)
BS, Shanghai Jiaotong University, Mechanical Engineering (2011)

My research lies at the intersection between physics (quantum mechanics, condensed matter, and statistical mechanics) and computer science. In particular there are three main directions:

1) Quantum computation and quantum information. We seek solutions to important computational problems that arise in physical sciences which are beyond what is feasible using classical computers.

2) Quantum complexity. This is an interesting intersection between condensed matter physics and theoretical computer science. In particular it has to do with fundamental questions such as how notions of computation based on quantum mechanics could change our understanding of the computational complexity landscape.

3) Statistical machine learning techniques. Many notions and techniques in large-scale machine learning and optimization algorithms are inspired by concepts in areas of physics such as statistical mechanics.

Selected Publications
Yudong Cao, Daniel Nagaj, "Perturbative gadgets without strong interactions", Quantum information and computation, arXiv:1408.5881 [quant-ph], 2014.
Yudong Cao, Anargyros Papageorgiou, Iasonas Petras, Joseph Traub, Sabre Kais, "Quantum algorithm and circuit design solving the Poisson equation", New Journal of Physics, Vol. 15, 013021. arXiv:1207.2485v2 [quant-ph], 2013.
Yudong Cao, Ryan Babbush, Jacob Biamonte, Sabre Kais, "Hamiltonian gadgets with reduced resource requirements", Physical Review A, 91, 012315. arXiv:1311.2555v2 [quant-ph], 2014.