Characterizing large-scale quantum computers via cycle benchmarking

By A. Erhard, Joel J. Wallman, L. Postler, Michael Meth, R. Stricker, E. Martinez, Philipp Schindler, Thomas Monz, Joseph Emerson, Rainer Blatt. Nature Communications, 2019

338 citations recorded by the source corpus.

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)%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$99.6(1)\%$$\end{document} for 2 qubits to 86(2)%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$86(2)\%$$\end{document} 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. Checking the quality of operations of quantum computers in a reliable and scalable way is still an open challenge. Here, the authors show how to characterise multi-qubit operations in a way that scales favourably with the system’s size, and demonstrate it on a 10-qubit ion-trap device.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/dcf04088b18c4e9ae2717302df26b01e8f71bc7c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Electrical and Electronic Engineering

Citations in the corpus

Probabilistic error cancellation with sparse Pauli–Lindblad models on noisy quantum processors (2022, background): “Learning procedures of noise in such circuit families for more general Pauli channels have been derived in [29–31].”
t|ket⟩: a retargetable compiler for NISQ devices (2020, background): “However it is well known that the noise channels in real devices are much more complex and more difficult to characterise [95, 60, 96].”
Detecting crosstalk errors in quantum information processors (2019, neutral): “crosstalk errors in quantum computing devices [6, 42, 18, 44, 51, 39, 16, 23, 49, 50, 48, 17, 2, 19, 21, 11, 35].”
Observation of separated dynamics of charge and spin in the Fermi-Hubbard model (2020, background): “Calibration of quantum gates is one of the most crucial steps in achieving high-fidelity quantum computation and its large-scale deployment [48–50].”