# Characterizing large-scale quantum computers via cycle benchmarking

## TL;DR

Cycle benchmarking is introduced as a scalable method to characterize errors in large-scale quantum computers, effectively capturing complex error sources and validated on ion-trap systems with up to 10 qubits.

## Contribution

The paper presents cycle benchmarking, a new scalable protocol for accurately characterizing local and global errors in multi-qubit quantum processors.

## Key findings

- Process fidelities range from 99.6% for 2 qubits to 86% for 10 qubits.
- Error rates per gate do not increase with system size.
- Cycle benchmarking effectively captures correlated noise and cross-talk.

## Abstract

Quantum computers promise to solve certain problems more efficiently than their digital counterparts. A major challenge towards practically useful quantum computing is characterizing and reducing the various errors that accumulate during an algorithm running on large-scale processors. Current characterization techniques are unable to adequately account for the exponentially large set of potential errors, including cross-talk and other correlated noise sources. Here we develop cycle benchmarking, a rigorous and practically scalable protocol for characterizing local and global errors across multi-qubit quantum processors. We experimentally demonstrate its practicality by quantifying such errors in non-entangling and entangling operations on an ion-trap quantum computer with up to 10 qubits, with total process fidelities for multi-qubit entangling gates ranging from 99.6(1)% for 2 qubits to 86(2)% for 10 qubits. Furthermore, cycle benchmarking data validates that the error rate per single-qubit gate and per two-qubit coupling does not increase with increasing system size.