Benchmarking near-term quantum computers via random circuit sampling
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.
Yunchao Liu Matthew Otten Roozbeh Bassirianjahromi Liang Jiang Bill Fefferman