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Learning the states of double quantum dot systems: A ray-based approach

ORAL

Abstract

Given the progress in the construction of multi-quantum dot (QD) arrays in both 1D and 2D [1,2], it is imperative to replace the current practice of manual tuning to a desirable electronic configuration with a standardized and automated method. Recently, we have experimentally realized an auto-tuning paradigm proposed by Kalantre et al. [3] that combines machine learning (ML) and optimization routines, with ConvNets used to characterize the state and charge configuration of single and double QD states from measurements via the conductance of a nearby charge sensor [4]. Now we expand on this work and propose a novel approach where we use 1D traces (“rays”) measured in multiple directions in the gate voltage space to describe the position of the features characterizing each state (i.e., to “fingerprint” the state space). Using these “fingerprints” instead of full-sized 2D scans we train an ML algorithm to differentiate between various state configurations. Here, we report the performance of the ray-based learning on experimental data and compare it with our image-based approach.

[1] Zajac et al., Phys. Rev. Appl. 6, 054013 (2016).
[2] Mukhopadhyay et al. Appl. Phys. Lett. 112, 183505 (2018).
[3] Kalantre et al. npj Quantum Inf. 5, 6 (2019).
[4] Zwolak et al. arXiv:1909.08030 (2019).

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Presenters

  • Justyna Zwolak

    National Institute of Standards and Technology, Gaithersburg, MD 20899, USA, National Institute of Standards and Technology

Authors

  • Justyna Zwolak

    National Institute of Standards and Technology, Gaithersburg, MD 20899, USA, National Institute of Standards and Technology

  • Thomas McJunkin

    University of Wisconsin - Madison, Department of Physics, University of Wisconsin-Madison, Madison, WI 53706, USA

  • Sandesh Kalantre

    Joint Quantum Institute, University of Maryland, College Park, MD 20742, USA, University of Maryland, College Park, Joint Center for Quantum Information and Computer Science, University of Maryland, College Park

  • Samuel Neyens

    Department of Physics, University of Wisconsin-Madison, Madison WI, USA, University of Wisconsin - Madison, Department of Physics, University of Wisconsin-Madison, Madison, WI 53706, USA

  • Evan R MacQuarrie

    University of Wisconsin - Madison, Department of Physics, University of Wisconsin-Madison, Madison WI, USA, Department of Physics, University of Wisconsin-Madison, Madison, WI 53706, USA

  • Lisa F Edge

    HRL, HRL laboratories, LLC, 3011 Malibu Canyon Road, Malibu, CA 90265, USA, HRL Laboratories, HRL Laboratories LLC, 3011 Malibu Canyon Road, Malibu, California 90265, USA, HRL Laboratories, LLC

  • Mark Alan Eriksson

    University of Wisconsin - Madison, Physics, University of Wisconsin - Madison, Madison, WI USA, Department of Physics, University of Wisconsin-Madison, Madison WI, USA, Department of Physics, University of Wisconsin-Madison, Madison, WI 53706, USA, University of Wisconsin-Madison

  • Jacob Taylor

    National Institute of Standards and Technology, Gaithersburg, MD 20899, USA, National Institute of Standards and Technology