Automated tuning of double quantum dots into specific charge states using neural networks

@article{Durrer2019AutomatedTO,
  title={Automated tuning of double quantum dots into specific charge states using neural networks},
  author={R. Durrer and B. Kratochwil and J. Koski and A. Landig and Christian Reichl and W. Wegscheider and T. Ihn and Eli{\vs}ka Greplov{\'a}},
  journal={arXiv: Mesoscale and Nanoscale Physics},
  year={2019}
}
  • R. Durrer, B. Kratochwil, +5 authors Eliška Greplová
  • Published 2019
  • Physics
  • arXiv: Mesoscale and Nanoscale Physics
  • While quantum dots are at the forefront of quantum device technology, tuning multi-dot systems requires a lengthy experimental process as multiple parameters need to be accurately controlled. This process becomes increasingly time-consuming and difficult to perform manually as the devices become more complex and the number of tuning parameters grows. In this work, we present a crucial step towards automated tuning of quantum dot qubits. We introduce an algorithm driven by machine learning that… CONTINUE READING
    11 Citations

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    References

    SHOWING 1-10 OF 44 REFERENCES
    Autonomous Tuning and Charge-State Detection of Gate-Defined Quantum Dots
    • 9
    • PDF
    Autotuning of double dot devices in situ with machine learning.
    • 15
    • PDF
    Automated tuning of inter-dot tunnel coupling in double quantum dots
    • 30
    • PDF
    Efficiently measuring a quantum device using machine learning
    • 25
    • PDF
    Fast hybrid silicon double-quantum-dot qubit.
    • 132
    • PDF
    Quantum Dot Arrays in Silicon and Germanium.
    • 23
    • PDF
    Tunable Hybrid Qubit in a GaAs Double Quantum Dot.
    • 48
    • PDF
    Electrical control of a long-lived spin qubit in a Si/SiGe quantum dot.
    • 282
    • PDF