Title:Benchmarking near-term quantum computers via random circuit sampling
Speaker: Yunchao Liu UC Berkeley
Time: 2021-12-29 10:00-2021-12-29 12:00
Venue:MMW S327+Tencent Meeting​(ID: 419-413-213​)


The increasing scale of near-term quantum hardware motivates the need for efficient noise characterization methods, since qubit and gate level techniques cannot capture crosstalk and correlated noise in many qubit systems. While scalable approaches, such as cycle benchmarking, are known for special classes of quantum circuits, the characterization of noise in general circuits with non-Clifford gates has been an unreachable task. We develop an algorithm that can sample-efficiently estimate the total amount of noise induced by a layer of arbitrary non-Clifford gates, including all crosstalks, and experimentally demonstrate the method on IBM Quantum hardware. Our algorithm is inspired by Google's quantum supremacy experiment and is based on random circuit sampling. In their paper, Google observed that their experimental linear cross entropy was consistent with a simple uncorrelated noise model, and claimed this coincidence indicated that the noise in their device was uncorrelated -- a key step in hardware development towards fault tolerance. As an application, we show that our result provides formal evidence to support such a conclusion.

Short Bio:

Yunchao Liu is a third-year PhD student in the Theory Group at UC Berkeley, advised by Prof. Umesh Vazirani. His research interests are in quantum information and computation. He received Bachelor's degree from Yao Class at Tsinghua University, where he worked with Prof. Xiongfeng Ma.