Abstract
Crosstalk is a leading source of failure in multiqubit quantum information processors. It can arise from a wide range of disparate physical phenomena, and can introduce subtle correlations in the errors experienced by a device. Several hardware characterization protocols are able to detect the presence of crosstalk, but few provide sufficient information to distinguish various crosstalk errors from one another. In this article we describe how gate set tomography, a protocol for detailed characterization of quantum operations, can be used to identify and characterize crosstalk errors in quantum information processors. We demonstrate our methods on a two-qubit trapped-ion processor and a two-qubit subsystem of a superconducting transmon processor.1 MoreReceived 17 March 2021Accepted 8 October 2021DOI:https://doi.org/10.1103/PRXQuantum.2.040338Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum tomographyQuantum Information
Highlights
Quantum information processors have demonstrated one- and two-qubit quantum operations with error rates below the threshold required for fault-tolerant quantum computation [1,2,3,4,5,6,7,8,9,10]
We demonstrate our methods on two U.S Department of Energy (DOE) sponsored quantum computing testbed platforms—the transmon-based Advanced Quantum Testbed (AQT) [55] and a prototype of the trapped-ion-based Quantum Scientific Computing Open User Testbed (QSCOUT) [56,57,58]—and we discuss and compare the errors observed on these two devices
The asymmetric crosstalk errors identified by our gate set tomography (GST) experiments are consistent with ac Stark shifts induced by the control fields
Summary
Quantum information processors have demonstrated one- and two-qubit quantum operations with error rates below the threshold required for fault-tolerant quantum computation [1,2,3,4,5,6,7,8,9,10]. Randomized methods—such as simultaneous randomized benchmarking (RB) [41], correlated RB [42], cycle benchmarking [43], and Pauli noise learning [44]—are among the most popular, as they are generally simple to implement and analyze These methods are typically sensitive to coherent errors at only second order [45,46,47], and rely on twirling techniques that obfuscate the underlying physical sources of observed errors. We can infer a surprising amount of information just from this evaluation, because we know exactly what kinds of crosstalk each model can describe, and for each model, we can quantify the amount of observed error that it failed to describe We use this analysis to select the simplest model that fits the data well, estimate its parameters to obtain “best fit” process matrices describing the gates and their errors, and analyze those process matrices in detail to understand the nature of the crosstalk errors.
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