Scalable Randomized Benchmarking of Quantum Computers Using Mirror Circuits

TLDR

Randomized benchmarking evaluates quantum gate performance, but current methods become infeasible beyond about five qubits. The authors aim to enable scalable, robust, and flexible randomized benchmarking of Clifford gates using a simple, customizable class of randomized mirror circuits. They employ randomized mirror circuits—a class of circuits that can be tailored—to benchmark Clifford gates across many qubits. The method accurately estimates the infidelity of an average many‑qubit logic layer, scales to simulations of up to 225 qubits with realistic error rates, and on a 16‑qubit cloud device it reveals and quantifies crosstalk errors in large circuits.

Abstract

The performance of quantum gates is often assessed using some form of randomized benchmarking. However, the existing methods become infeasible for more than approximately five qubits. Here we show how to use a simple and customizable class of circuits-randomized mirror circuits-to perform scalable, robust, and flexible randomized benchmarking of Clifford gates. We show that this technique approximately estimates the infidelity of an average many-qubit logic layer, and we use simulations of up to 225 qubits with physically realistic error rates in the range 0.1%-1% to demonstrate its scalability. We then use up to 16 physical qubits of a cloud quantum computing platform to demonstrate that our technique can reveal and quantify crosstalk errors in many-qubit circuits.