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Understanding Materials-Level Sources of Performance Variations in Superconducting Qubits

ORAL · Invited

Abstract

Advances in our understanding of materials have played a crucial role in driving recent increases in achievable coherence times and gate fidelities in superconducting transmon qubits. This includes identifying defects, impurities, interfaces, and surfaces that can potentially introduce two-level systems (TLS) or non-TLS dissipation as well as implementing new strategies to mitigate the deleterious effects introduced by these sources of quantum decoherence. As part of the Superconducting Materials and Systems (SQMS) center, a DOE National Quantum Information Science Research Center, we have conducted a comprehensive and coordinated blind study with qubit chips of known performance variations probed across multiple institutions, including Ames Lab, Northwestern, and Fermilab, in an effort that brings together over 40 researchers in the materials science and superconducting materials domains. These qubits have been interrogated with various unique non-destructive and destructive characterization techniques to pinpoint the underlying materials-level sources of device-to-device performance variation giving rise to decoherence. Preliminary findings suggest a correlation between variations in magnetic flux penetration, surface oxide quantities, and sidewall geometries with microwave loss across different devices. These insights provide a pathway to not only reduce performance variations but also to fabricate devices with performance metrics beyond state-of-the-art values.

Publication: Full author list: <br>Akshay A. Murthy1, Mustafa Bal1, Shaojiang Zhu1, Francesco Crisa1, Jaeyel Lee1, Zuhawn Sung1, Andrei Lunin1, Daniel Bafia1, Cameron J. Kopas2, Ella O. Lachman2, Josh Y. Mutus2, Hilal Cansizoglu2, Jayss Marshall2, David P. Pappas2, Jin-Su Oh3, Lin Zhou3, Kamal Joshi3, Amlan Datta3, Sunil Ghimire3, Makariy Tanatar3, Ruslan Prozorov3, Richard Kim3, Sam Haeuser3, Joong-mok Park3, Randall K. Chan, Jigang Wang3, Matthew J. Kramer3, Dominic P. Goronzy4, Mitchell J. Walker4, Celeo D. Matute Diaz4, Mark C. Hersam4, David A. Garcia-Wetten4, William Mah4, Michael J. Bedzyk4, Peter G. Lim4, Roberto dos Reis4, Vinayak P. Dravid4, Dieter Isheim4, David Seidman4, Yanpei Deng4, Maxwell Wisne4, Venkat Chandrasekhar4, Alexander Romanenko1, and Anna Grassellino1<br><br>1Fermilab; 2Rigetti Computing; 3Ames National Lab; 4Northwestern University

Presenters

  • Akshay Murthy

    Fermi National Accelerator Laboratory, Fermi National Accelerator Laboratory (Fermilab), Fermilab

Authors

  • Akshay Murthy

    Fermi National Accelerator Laboratory, Fermi National Accelerator Laboratory (Fermilab), Fermilab

  • Mustafa Bal

    Fermi National Accelerator Laboratory, Fermi National Accelerator Laboratory (Fermilab), Fermilab

  • Shaojiang Zhu

    Fermi National Accelerator Laboratory (Fermilab), Fermilab, Fermi National Accelerator Laboratory

  • Francesco Crisa

    Fermi National Accelerator Laboratory, Fermilab, Fermilab, SQMS, Fermi National Accelerator Laboratory (Fermilab)

  • Matthew J Kramer

    Ames National Laboratory

  • Lin Zhou

    Ames National Laboratory

  • Dominic Pascal Goronzy

    Northwestern University

  • Michael J Bedzyk

    Northwestern University

  • Cameron J Kopas

    Rigetti Computing

  • Ella O Lachman

    Rigetti Computing

  • Alexander Romanenko

    Fermi National Accelerator Laboratory, Fermi National Accelerator Laboratory (Fermilab), Fermilab

  • Anna Grassellino

    Fermi National Accelerator Laboratory, Fermi National Accelerator Laboratory (Fermilab), Fermilab