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 $$99.6(1)\%$$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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Nov 25, 2019
- Source ID
- 10.1038/s41467-019-13068-7
Entities
People
- Alexander Erhard
- Esteban A Martinez
- Joel J Wallman
- Joseph Emerson
- Lukas Postler
- M. Meth
- Philipp Schindler
- Rainer Blatt
- Roman Stricker
- Thomas Monz
Organizations
- Army Research Office
- Austrian Research Promotion Agency
- Austrian Science Fund
- Intelligence Advanced Research Projects Activity