Characterizing large-scale quantum computers via cycle benchmarking

Alexander Erhard, Joel J. Wallman (), Lukas Postler, Michael Meth, Roman Stricker, Esteban A. Martinez, Philipp Schindler, Thomas Monz (), Joseph Emerson and Rainer Blatt
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Alexander Erhard: University of Innsbruck
Joel J. Wallman: University of Waterloo
Lukas Postler: University of Innsbruck
Michael Meth: University of Innsbruck
Roman Stricker: University of Innsbruck
Esteban A. Martinez: University of Innsbruck
Philipp Schindler: University of Innsbruck
Thomas Monz: University of Innsbruck
Joseph Emerson: University of Waterloo
Rainer Blatt: University of Innsbruck

Nature Communications, 2019, vol. 10, issue 1, 1-7

Abstract: 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 $$99.6(1)\%$$99.6(1)% for 2 qubits to $$86(2)\%$$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.

Date: 2019
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DOI: 10.1038/s41467-019-13068-7

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