IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v15y2024i1d10.1038_s41467-024-52629-3.html
   My bibliography  Save this article

Robustly learning the Hamiltonian dynamics of a superconducting quantum processor

Author

Listed:
  • Dominik Hangleiter

    (University of Maryland and NIST
    University of Maryland and NIST
    Freie Universität Berlin)

  • Ingo Roth

    (Freie Universität Berlin
    Technology Innovation Institute (TII))

  • Jonáš Fuksa

    (Freie Universität Berlin)

  • Jens Eisert

    (Freie Universität Berlin
    Helmholtz-Zentrum Berlin für Materialien und Energie)

  • Pedram Roushan

    (Google Quantum AI)

Abstract

Precise means of characterizing analog quantum simulators are key to developing quantum simulators capable of beyond-classical computations. Here, we precisely estimate the free Hamiltonian parameters of a superconducting-qubit analog quantum simulator from measured time-series data on up to 14 qubits. To achieve this, we develop a scalable Hamiltonian learning algorithm that is robust against state-preparation and measurement (SPAM) errors and yields tomographic information about those SPAM errors. The key subroutines are a novel super-resolution technique for frequency extraction from matrix time-series, tensorESPRIT, and constrained manifold optimization. Our learning results verify the Hamiltonian dynamics on a Sycamore processor up to sub-MHz accuracy, and allow us to construct a spatial implementation error map for a grid of 27 qubits. Our results constitute an accurate implementation of a dynamical quantum simulation that is precisely characterized using a new diagnostic toolkit for understanding, calibrating, and improving analog quantum processors.

Suggested Citation

  • Dominik Hangleiter & Ingo Roth & Jonáš Fuksa & Jens Eisert & Pedram Roushan, 2024. "Robustly learning the Hamiltonian dynamics of a superconducting quantum processor," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-52629-3
    DOI: 10.1038/s41467-024-52629-3
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-024-52629-3
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-024-52629-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Sepehr Ebadi & Tout T. Wang & Harry Levine & Alexander Keesling & Giulia Semeghini & Ahmed Omran & Dolev Bluvstein & Rhine Samajdar & Hannes Pichler & Wen Wei Ho & Soonwon Choi & Subir Sachdev & Marku, 2021. "Quantum phases of matter on a 256-atom programmable quantum simulator," Nature, Nature, vol. 595(7866), pages 227-232, July.
    2. Manoj K. Joshi & Christian Kokail & Rick Bijnen & Florian Kranzl & Torsten V. Zache & Rainer Blatt & Christian F. Roos & Peter Zoller, 2023. "Exploring large-scale entanglement in quantum simulation," Nature, Nature, vol. 624(7992), pages 539-544, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hu, Jie-Ru & Zhang, Zuo-Yuan & Liu, Jin-Ming, 2024. "Implementation of three-qubit Deutsch-Jozsa algorithm with pendular states of polar molecules by optimal control," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 635(C).
    2. Luheng Zhao & Michael Dao Kang Lee & Mohammad Mujahid Aliyu & Huanqian Loh, 2023. "Floquet-tailored Rydberg interactions," Nature Communications, Nature, vol. 14(1), pages 1-7, December.
    3. Jin Ming Koh & Tommy Tai & Ching Hua Lee, 2024. "Realization of higher-order topological lattices on a quantum computer," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    4. Shankar G. Menon & Noah Glachman & Matteo Pompili & Alan Dibos & Hannes Bernien, 2024. "An integrated atom array-nanophotonic chip platform with background-free imaging," Nature Communications, Nature, vol. 15(1), pages 1-7, December.
    5. S. K. Kanungo & J. D. Whalen & Y. Lu & M. Yuan & S. Dasgupta & F. B. Dunning & K. R. A. Hazzard & T. C. Killian, 2022. "Realizing topological edge states with Rydberg-atom synthetic dimensions," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    6. Daniel Stilck França & Liubov A. Markovich & V. V. Dobrovitski & Albert H. Werner & Johannes Borregaard, 2024. "Efficient and robust estimation of many-qubit Hamiltonians," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    7. Matthew J. O’Rourke & Garnet Kin-Lic Chan, 2023. "Entanglement in the quantum phases of an unfrustrated Rydberg atom array," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    8. Liang Xiang & Jiachen Chen & Zitian Zhu & Zixuan Song & Zehang Bao & Xuhao Zhu & Feitong Jin & Ke Wang & Shibo Xu & Yiren Zou & Hekang Li & Zhen Wang & Chao Song & Alexander Yue & Justine Partridge & , 2024. "Enhanced quantum state transfer by circumventing quantum chaotic behavior," Nature Communications, Nature, vol. 15(1), pages 1-8, December.
    9. Yue Wu & Shimon Kolkowitz & Shruti Puri & Jeff D. Thompson, 2022. "Erasure conversion for fault-tolerant quantum computing in alkaline earth Rydberg atom arrays," Nature Communications, Nature, vol. 13(1), pages 1-7, December.
    10. Katrina Barnes & Peter Battaglino & Benjamin J. Bloom & Kayleigh Cassella & Robin Coxe & Nicole Crisosto & Jonathan P. King & Stanimir S. Kondov & Krish Kotru & Stuart C. Larsen & Joseph Lauigan & Bri, 2022. "Assembly and coherent control of a register of nuclear spin qubits," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    11. Y.-H. Hou & Y.-J. Yi & Y.-K. Wu & Y.-Y. Chen & L. Zhang & Y. Wang & Y.-L. Xu & C. Zhang & Q.-X. Mei & H.-X. Yang & J.-Y. Ma & S.-A. Guo & J. Ye & B.-X. Qi & Z.-C. Zhou & P.-Y. Hou & L.-M. Duan, 2024. "Individually addressed entangling gates in a two-dimensional ion crystal," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    12. Ya-Dong Wu & Yan Zhu & Yuexuan Wang & Giulio Chiribella, 2024. "Learning quantum properties from short-range correlations using multi-task networks," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    13. Spencer D. Fallek & Vikram S. Sandhu & Ryan A. McGill & John M. Gray & Holly N. Tinkey & Craig R. Clark & Kenton R. Brown, 2024. "Rapid exchange cooling with trapped ions," Nature Communications, Nature, vol. 15(1), pages 1-9, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-52629-3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.