Resonant Coupling Parameter Estimation with Superconducting Qubits

Bejanin, JH; Earnest, CT; Sanders, YR; Mariantoni, M

Resonant Coupling Parameter Estimation with Superconducting Qubits

Bejanin, JH Earnest, CT Sanders, YR Mariantoni, M

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Publisher:: American Physical Society
Publication Type:: Journal Article
Citation:: PRX Quantum, 2021, 2, (4), pp. 1-18
Issue Date:: 2021-11-30

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Field	Value	Language
dc.contributor.author	Bejanin, JH
dc.contributor.author	Earnest, CT
dc.contributor.author	Sanders, YR
dc.contributor.author	Mariantoni, M
dc.date.accessioned	2022-05-20T06:16:25Z
dc.date.available	2022-05-20T06:16:25Z
dc.date.issued	2021-11-30
dc.identifier.citation	PRX Quantum, 2021, 2, (4), pp. 1-18
dc.identifier.issn	2691-3399
dc.identifier.issn	2691-3399
dc.identifier.uri	http://hdl.handle.net/10453/157556
dc.description.abstract	Today’s quantum computers are composed of tens of qubits interacting with each other and the environment in increasingly complex networks. To achieve the best possible performance when operating such systems, it is necessary to have accurate knowledge of all parameters in the quantum computer Hamiltonian. In this paper, we demonstrate theoretically and experimentally a method to efficiently learn the parameters of resonant interactions for quantum computers consisting of frequency-tunable superconducting qubits. Such interactions include, for example, those with other qubits, resonators, two-level systems, or other wanted or unwanted modes. Our method is based on a significantly improved swap spectroscopy calibration and consists of an offline data collection algorithm, followed by an online Bayesian learning algorithm. The purpose of the offline algorithm is to detect and coarsely estimate resonant interactions from a state of zero knowledge. It produces a quadratic speedup in the scaling of the number of measurements. The online algorithm subsequently refines the estimate of the parameters to accuracy comparable with that of traditional swap spectroscopy calibration but in constant time. We perform an experiment implementing our technique with a superconducting qubit. By combining both algorithms, we observe a reduction of the calibration time by 1 order of magnitude. Our method will improve present medium-scale superconducting quantum computers and will also scale up to larger systems. Finally, the two algorithms presented here can be readily adopted by communities working on different physical implementations of quantum computing architectures.
dc.language	English
dc.publisher	American Physical Society
dc.relation.ispartof	PRX Quantum
dc.relation.isbasedon	10.1103/PRXQuantum.2.040343
dc.rights	Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Fur- ther distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.rights	" Bejanin, J. H.; Earnest, C. T.; Sanders, Y. R.; Mariantoni, M. \| Resonant Coupling Parameter Estimation with Superconducting Qubits , PRX Quantum, Vol. 2, no. 4. Pp 1-18 2021 "Copyright (2021) by the American Physical Society."
dc.rights	Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Fur- ther distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.
dc.title	Resonant Coupling Parameter Estimation with Superconducting Qubits
dc.type	Journal Article
utslib.citation.volume	2
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	recently_added	*
pubs.consider-herdc	false
dc.date.updated	2022-05-20T06:16:22Z
pubs.issue	4
pubs.publication-status	Published
pubs.volume	2
utslib.citation.issue	4

Abstract:

Today’s quantum computers are composed of tens of qubits interacting with each other and the environment in increasingly complex networks. To achieve the best possible performance when operating such systems, it is necessary to have accurate knowledge of all parameters in the quantum computer Hamiltonian. In this paper, we demonstrate theoretically and experimentally a method to efficiently learn the parameters of resonant interactions for quantum computers consisting of frequency-tunable superconducting qubits. Such interactions include, for example, those with other qubits, resonators, two-level systems, or other wanted or unwanted modes. Our method is based on a significantly improved swap spectroscopy calibration and consists of an offline data collection algorithm, followed by an online Bayesian learning algorithm. The purpose of the offline algorithm is to detect and coarsely estimate resonant interactions from a state of zero knowledge. It produces a quadratic speedup in the scaling of the number of measurements. The online algorithm subsequently refines the estimate of the parameters to accuracy comparable with that of traditional swap spectroscopy calibration but in constant time. We perform an experiment implementing our technique with a superconducting qubit. By combining both algorithms, we observe a reduction of the calibration time by 1 order of magnitude. Our method will improve present medium-scale superconducting quantum computers and will also scale up to larger systems. Finally, the two algorithms presented here can be readily adopted by communities working on different physical implementations of quantum computing architectures.

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http://hdl.handle.net/10453/157556