Abstract: Variational algorithms can be used to efficiently solve classically intractable problems on near-future quantum computers. However, their potential is limited by hardware errors. It is therefore crucial to develop efficient ways to mitigate these errors. In this talk, we introduce variational quantum simulation for finding static energy spectra and simulating dynamics of many-body systems, such as the electronic structure in chemistry. We also introduce the recently proposed error mitigation methods for suppressing errors in shallow circuits. Variational quantum simulation supported with error mitigation may become the first application of quantum computing with noisy intermediate scale quantum hardware.
Bio: Dr. Xiao Yuan received his Bachelor in theoretical physics (major) and computer science (minor) from Peking University in 2012. He received his Ph.D. degree in Physics from Tsinghua University in 2017. From 2017, Xiao conducted research as a postdoctoral fellow at University of Oxford. Xiao’s research interests span the wide spectrum of quantum information science, from fundamental quantum information to algorithms for near-term quantum computers. He predominantly worked on theoretical aspects of quantum information, specifically, measuring and quantifying ‘quantumness’, random number generation and self-testing quantum information. His current research is focused on pragmatic approaches to quantum computing, among which algorithms for simulation and machine learning on near-future quantum computers in particular. Xiao’s research explored the interconnectedness of these seemingly disparate topics, and ways to realize them experimemtally.