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Wagner, Lukas

Lukas Wagner

Herr MSc

PhD-Student

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+49 711 685 63878
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Allmandring 3
70569 Stuttgart
Deutschland
Raum: 0.088

Publikationen

2024
1. Corcione, E., Jakob, F., Wagner, L., Joos, R., Bisquerra, A., Schmidt, M., Wieck, A. D., Ludwig, A., Jetter, M., Portalupi, S. L., Michler, P., & Tarín, C. (2024). Machine learning enhanced evaluation of semiconductor quantum dots. Scientific Reports, 14(1), Article 1. https://doi.org/10.1038/s41598-024-54615-7
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  @article{Corcione2024, abstract = {A key challenge in quantum photonics today is the efficient and on-demand generation of high-quality single photons and entangled photon pairs. In this regard, one of the most promising types of emitters are semiconductor quantum dots, fluorescent nanostructures also described as artificial atoms. The main technological challenge in upscaling to an industrial level is the typically random spatial and spectral distribution in their growth. Furthermore, depending on the intended application, different requirements are imposed on a quantum dot, which are reflected in its spectral properties. Given that an in-depth suitability analysis is lengthy and costly, it is common practice to pre-select promising candidate quantum dots using their emission spectrum. Currently, this is done by hand. Therefore, to automate and expedite this process, in this paper, we propose a data-driven machine-learning-based method of evaluating the applicability of a semiconductor quantum dot as single photon source. For this, first, a minimally redundant, but maximally relevant feature representation for quantum dot emission spectra is derived by combining conventional spectral analysis with an autoencoding convolutional neural network. The obtained feature vector is subsequently used as input to a neural network regression model, which is specifically designed to not only return a rating score, gauging the technical suitability of a quantum dot, but also a measure of confidence for its evaluation. For training and testing, a large dataset of self-assembled InAs/GaAs semiconductor quantum dot emission spectra is used, partially labelled by a team of experts in the field. Overall, highly convincing results are achieved, as quantum dots are reliably evaluated correctly. Note, that the presented methodology can account for different spectral requirements and is applicable regardless of the underlying photonic structure, fabrication method and material composition. We therefore consider it the first step towards a fully integrated evaluation framework for quantum dots, proving the use of machine learning beneficial in the advancement of future quantum technologies.}, author = {Corcione, Emilio and Jakob, Fabian and Wagner, Lukas and Joos, Raphael and Bisquerra, Andre and Schmidt, Marcel and Wieck, Andreas D. and Ludwig, Arne and Jetter, Michael and Portalupi, Simone L. and Michler, Peter and Tar{\'i}n, Cristina}, day = 20, doi = {10.1038/s41598-024-54615-7}, issn = {2045-2322}, journal = {Scientific Reports}, month = {02}, number = 1, pages = 4154, title = {Machine learning enhanced evaluation of semiconductor quantum dots}, url = {https://doi.org/10.1038/s41598-024-54615-7}, volume = 14, year = 2024 }
  Link
  https://doi.org/10.1038/s41598-024-54615-7
2. Thomas, S. E., Wagner, L., Joos, R., Sittig, R., Nawrath, C., Burdekin, P., de Buy Wenniger, I. M., Rasiah, M. J., Huber-Loyola, T., Sagona-Stophel, S., Höfling, S., Jetter, M., Michler, P., Walmsley, I. A., Portalupi, S. L., & Ledingham, P. M. (2024). Deterministic storage and retrieval of telecom light from a quantum dot single-photon source interfaced with an atomic quantum memory. Science Advances, 10(15), Article 15. https://doi.org/10.1126/sciadv.adi7346
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  @article{doi:10.1126/sciadv.adi7346, abstract = {A hybrid interface of solid-state single-photon sources and atomic quantum memories is a long sought-after goal in photonic quantum technologies. Here, we demonstrate deterministic storage and retrieval of light from a semiconductor quantum dot in an atomic ensemble quantum memory at telecommunications wavelengths. We store single photons from an indium arsenide quantum dot in a high-bandwidth rubidium vapor–based quantum memory, with a total internal memory efficiency of (12.9 ± 0.4)\%. The signal-to-noise ratio of the retrieved light field is 18.2 ± 0.6, limited only by detector dark counts. A quantum dot single-photon source and an atomic quantum memory are key interface components for future quantum networks.}, author = {Thomas, Sarah E. and Wagner, Lukas and Joos, Raphael and Sittig, Robert and Nawrath, Cornelius and Burdekin, Paul and de Buy Wenniger, Ilse Maillette and Rasiah, Mikhael J. and Huber-Loyola, Tobias and Sagona-Stophel, Steven and Höfling, Sven and Jetter, Michael and Michler, Peter and Walmsley, Ian A. and Portalupi, Simone L. and Ledingham, Patrick M.}, doi = {10.1126/sciadv.adi7346}, eprint = {https://www.science.org/doi/pdf/10.1126/sciadv.adi7346}, journal = {Science Advances}, number = 15, pages = {eadi7346}, title = {Deterministic storage and retrieval of telecom light from a quantum dot single-photon source interfaced with an atomic quantum memory}, url = {https://www.science.org/doi/abs/10.1126/sciadv.adi7346}, volume = 10, year = 2024 }
  Link
  https://www.science.org/doi/abs/10.1126/sciadv.adi7346
2023
1. Strobel, T., Weber, J. H., Schmidt, M., Wagner, L., Engel, L., Jetter, M., Wieck, A. D., Portalupi, S. L., Ludwig, A., & Michler, P. (2023). A Unipolar Quantum Dot Diode Structure for Advanced Quantum Light Sources. Nano Letters, 23(14), Article 14. https://doi.org/10.1021/acs.nanolett.3c01658
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  BibTeX
  @article{Strobel_2023, author = {Strobel, Tim and Weber, Jonas H. and Schmidt, Marcel and Wagner, Lukas and Engel, Lena and Jetter, Michael and Wieck, Andreas D. and Portalupi, Simone L. and Ludwig, Arne and Michler, Peter}, doi = {10.1021/acs.nanolett.3c01658}, journal = {Nano Letters}, month = {07}, number = 14, pages = {6574--6580}, publisher = {American Chemical Society ({ACS})}, title = {A Unipolar Quantum Dot Diode Structure for Advanced Quantum Light Sources}, url = {https://doi.org/10.1021%2Facs.nanolett.3c01658}, volume = 23, year = 2023 }
  Link
  https://doi.org/10.1021%2Facs.nanolett.3c01658

Lukas Wagner

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2024

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2023

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Lukas Wagner

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Publikationen

2024

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2023

BibTeX

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