Hardware-efficient random circuits to classify noise in a multi-qubit system

TL;DR

This paper extends a multi-qubit benchmarking method called Binned Output Generation (BOG) to effectively distinguish between coherent and incoherent noise sources in multi-qubit systems, including regimes beyond current standard techniques.

Contribution

The authors adapt and experimentally validate BOG for multi-qubit systems, enabling discrimination of noise types at larger scales where existing methods struggle.

Findings

BOG accurately distinguishes coherent from incoherent noise.

The technique scales to six qubits, surpassing the capabilities of standard RB.

Measured coherent noise scales with injected noise, confirming method validity.

Abstract

In this work we extend a multi-qubit benchmarking technique known as the Binned Output Generation (BOG) in order to discriminate between coherent and incoherent noise sources in the multi-qubit regime. While methods exist to discriminate coherent from incoherent noise at the single and few-qubit level, these methods scale poorly beyond a few qubits or must make assumptions about the form of the noise. On the other end of the spectrum, system-level benchmarking techniques exist, but fail to discriminate between coherent and incoherent noise sources. We experimentally verify the BOG against Randomized Benchmarking (RB) (the industry standard benchmarking technique) in the two-qubit regime, then apply this technique to a six qubit linear chain, a regime currently inaccessible to RB. In this experiment we inject an instantaneous coherent Z-type noise on each qubit and demonstrate that the measured coherent noise scales correctly with the magnitude of the injected noise, while the measured incoherent noise remains unchanged as expected. This demonstrates a robust technique to measure coherent errors in a variety of hardware.