Southwest Quantum Information and Technology

Scaling randomized benchmarking into the quantum advantage regime

Presenting Author: Jordan Hines, University of California Berkeley
Contributing Author(s): Daniel Hothem, Marie Lu, Ravi K. Naik, Akel Hashim,Jean-Loup Ville, Brad Mitchell, John Mark Kriekebaum, David I. Santiago, Stefan Seritan, Erik Nielsen, Robin Blume-Kohout, Kevin Young, Irfan Siddiqi, Birgitta Whaley, Timothy Proctor

Randomized benchmarks are widely used for quantifying the performance of quantum processors. However, most existing protocols are limited in scalability, often due to requiring classical computations that scale exponentially in the number of qubits. Here, we introduce two highly scalable randomized benchmarking methods with low classical computation cost. Our methods modify standard randomized benchmarking and cross entropy benchmarking, connecting those methods and preserving their core strengths. Our first method uses randomized mirror circuits to enable benchmarking a large class of universal gate sets. Our second method benchmarks Clifford gates by applying a streamlined fidelity estimation method to random circuits. We use theory, simulations, and experiments to show that our methods reliably estimate the average error rate of random circuit layers. We demonstrate randomized benchmarking of universal gate sets on four qubits of the Advanced Quantum Testbed, including a gate set containing a controlled S gate and its inverse, and we investigate the impact of non-Clifford gates on the observed error rate. Finally, we demonstrate that our methods scale to many qubits with experiments on a 27-qubit IBM Q processor, and quantify the contribution of crosstalk to the error rate. This work was supported in part by the LDRD program at SNL and by the US DOE SC/ASCR’s Quantum Testbeds for Science Program. SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525.

Read this article online: https://arxiv.org/abs/2207.07272

(Session 13 : from 4:15 pm - 4:45 pm)