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Trapped Ion Quantum Computing Quantum Machine Learning

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

arXiv
Authors: Renato Durrer, Benedikt Kratochwil, Jonne V. Koski, Andreas J. Landig, Christian Reichl, Werner Wegscheider, Thomas Ihn, Eliska Greplova

Year

2019

Paper ID

14293

Status

Preprint

Abstract Read

~2 min

Abstract Words

120

Citations

N/A

Abstract

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 uses a small number of coarse-grained measurements as its input and tunes the quantum dot system into a pre-selected charge state. We train and test our algorithm on a GaAs double quantum dot device and we consistently arrive at the desired state or its immediate neighborhood.

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  • This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
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  • 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...

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References & Citation Signals

[1] DOI https://doi.org/arXiv:1912.02777 [2] arXiv https://arxiv.org/abs/1912.02777 [3] Publisher https://arxiv.org/abs/1912.02777

Local Citation Graph (Related-Paper Links)

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