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Building a Long-Lived 3D Multimode Quantum Processing Unit with TESLA Cavities

ORAL

Abstract

Developing long-lived quantum processing units (QPUs) capable of supporting high-fidelity quantum operations is a crucial challenge on the path toward fault-tolerant quantum computing. TESLA-shaped superconducting RF (SRF) cavities, known for photon relaxation times on the order of seconds, provide an excellent foundation for 3D QPUs and quantum memory. This talk presents a novel design that leverages TESLA cavity modes coupled to ancillary transmon qubits, optimized to preserve coherence and control. By carefully engineering the package geometry, optimizing Hamiltonian parameters, and minimizing lossy participation ratios, we achieve photon relaxation times of over 16 ms and 20 ms for the two cavity modes, representing a significant improvement over previous multimode quantum memories. Despite the reduced coupling between the qubit and cavity modes, which is necessary to preserve long lifetimes, the platform supports robust and universal control schemes that are not limited by low coupling strength. We will also discuss how this architecture can lead to scalable, modular quantum computing systems.

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Publication: A Long-Lived Multimode Quantum Processing Unit with Versatile Universal Control, in preparation.

Presenters

  • Yao Lu

    Fermi National Accelerator Laboratory (Fermilab), Fermilab

Authors

  • Yao Lu

    Fermi National Accelerator Laboratory (Fermilab), Fermilab

  • Taeyoon Kim

    Northwestern University

  • Tanay Roy

    Fermi National Accelerator Laboratory (Fermilab), Fermilab

  • Xinyuan You

    Fermi National Accelerator Laboratory (Fermilab), Fermilab

  • Oleg V Pronitchev

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

  • Mustafa Bal

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

  • Sabrina Garattoni

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

  • Francesco Crisa

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

  • Daniel Bafia

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

  • Roman M Pilipenko

    Fermilab, Fermi National Accelerator Laboratory

  • Paul Heidler

    Fermi National Accelerator Laboratory (Fermilab), Fermilab

  • Andy C. Y. Li

    Fermi National Accelerator Laboratory (Fermilab), Fermilab

  • Yunwei Lu

    Northwestern University

  • David V Zanten

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

  • Silvia Zorzetti

    Fermi National Accelerator Laboratory (Fermilab), Fermilab

  • Roni Harnik

    Fermi National Accelerator Laboratory (Fermilab), Fermilab

  • Akshay Murthy

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

  • Shaojiang Zhu

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

  • André Vallières

    Northwestern University

  • Ziwen Huang

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

  • Jens Koch

    Northwestern University

  • Anna Grassellino

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

  • Srivatsan Chakram

    Rutgers University, Rutgers, The State University of New Jersey

  • Alexander Romanenko

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