RQC Seminar

11th RQC Seminar

  • Speaker

    Prof. Anton Frisk Kockum
    (Department of Microtechnology and Nanoscience, Chalmers University of Technology.)

  • Date

    16:00-17:00 (JST), August 16, 2022 (Tuesday)

  • Venue

    Hybrid (Zoom / 2F Large Conference Room, Admin. Headquarters Bldg. RIKEN Wako branch)

  • Title

    Quantum state and process tomography with machine learning and gradient descent

  • Inquiries

    rqc_info[at]ml.riken.jp

Abstract
The ability to quickly and accurately characterise quantum states and dynamics is crucial for the development of quantum technologies. However, the problem of learning a general quantum state or process has exponential complexity in the size of the quantum system. In this talk, I will present some recent progress we have made for both quantum state and process tomography. For state tomography, I will show how generative adversarial neural networks can outperform standard methods in terms of both the amount of time and data needed [1,2]. For process tomography, I will show how optimization using constrained gradient descent can work both for instances with little data and for larger systems, regimes which previously required two different methods [3].

References
[1] S. Ahmed et al., Phys. Rev. Lett. 127, 140502 (2021)
[2] S. Ahmed et al., Phys. Rev. Res. 3, 033278 (2021)
[3] S. Ahmed et al., in preparation.


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