Characterizing large-scale quantum computers via cycle benchmarking.

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, and total process fidelities for multi-qubit entangling gates ranging from [Formula: see text] for 2 qubits to [Formula: see text] 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.

journal_name

Nat Commun

journal_title

Nature communications

authors

Erhard A,Wallman JJ,Postler L,Meth M,Stricker R,Martinez EA,Schindler P,Monz T,Emerson J,Blatt R

doi

10.1038/s41467-019-13068-7

subject

Has Abstract

pub_date

2019-11-25 00:00:00

pages

5347

issue

1

issn

2041-1723

pii

10.1038/s41467-019-13068-7

journal_volume

10

pub_type

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