Scalable fast benchmarking for individual quantum gates with local twirling

TL;DR

This paper introduces efficient local twirling-based benchmarking protocols for accurately estimating the fidelity of individual multi-qubit quantum gates, including non-Clifford gates, with significantly reduced sampling complexity.

Contribution

It proposes novel character-cycle and character-average benchmarking methods using local twirling, enabling scalable and reliable fidelity estimation for a broad class of quantum gates.

Findings

Protocols accurately estimate process fidelities of multi-qubit gates.

Numerical demonstrations show high efficiency and reliability.

Character-average benchmarking reduces sampling complexity by three orders of magnitude.

Abstract

With the development of controllable quantum systems, fast and practical characterization for multi-qubit gates is essential for building high-fidelity quantum computing devices. The usual way to fulfill this requirement via randomized benchmarking asks for the complicated implementation of numerous multi-qubit twirling gates. How to efficiently and reliably estimate the fidelity of a quantum process remains an open problem. In this work, we propose a character-cycle benchmarking protocol and a character-average benchmarking protocol only using local twirling gates to estimate the process fidelity of an individual multi-qubit operation. Our protocols can characterize a large class of quantum gates including and beyond the Clifford group via the local gauge transformation, which forms a universal gate set for quantum computing. We numerically demonstrate our protocols for a non-Clifford gate -- controlled-$(TX)$ and a Clifford gate -- five-qubit quantum error-correcting encoding circuit. The numerical results show that our protocols can efficiently and reliably characterize the gate process fidelities. Compared with the cross-entropy benchmarking, the simulation results show that the character-average benchmarking achieves three orders of magnitude improvements in terms of sampling complexity.