Photonic Quantum Technologies

Benyoucef, M.; Bennett, A. J.; Götzinger, Stephan; Lu, Chao‐Yang

doi:10.1002/qute.202000007

Cited by 4 publications

(2 citation statements)

References 26 publications

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“…for long-distance communication through optical fibres 9 , 10 . Besides, photons are comparatively easy to manipulate, making photonic setups experimentally very accessible 11 , 12 . Obviously, the development of an efficient and on-demand single photon source is key, with brightness, purity and indistinguishability of the emitted photons taking priority 13 , 14 .…”

Section: Introductionmentioning

confidence: 99%

Machine learning enhanced evaluation of semiconductor quantum dots

Corcione,

Jakob,

Wagner

et al. 2024

Sci Rep

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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.

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Section: Introductionmentioning

confidence: 99%

Machine learning enhanced evaluation of semiconductor quantum dots

Corcione,

Jakob,

Wagner

et al. 2024

Sci Rep

View full text Add to dashboard Cite

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“…Bright, polarized, and high-purity single-photon sources are essential elements in long-distance quantum communication − and optical quantum computation. − Semiconductor quantum dots (QDs), due to their compatibility with existing semiconductor processes, have received long-term attention in realizing solid-state optimal single-photon sources. In recent years, QDs combined with state-of-the-art photonic cavities have achieved remarkable progress in brightness, , single-photon purity, , indistinguishability, ,, and entanglement fidelity. − However, the spectral range (780 and 900 nm) of these achievements falls outside the telecom wavelengths, resulting in high photon loss and dispersion over long-distance fiber transmission.…”

mentioning

confidence: 99%

Polarized and Bright Telecom C-Band Single-Photon Source from InP-Based Quantum Dots Coupled to Elliptical Bragg Gratings

Ge,

Chung,

et al. 2024

Nano Lett.

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Bright, polarized, and high-purity single-photon sources in telecom wavelengths are crucial components in longdistance quantum communication, optical quantum computation, and quantum networks. Semiconductor InAs/InP quantum dots (QDs) combined with photonic cavities provide a competitive path, leading to optimal single-photon sources in this range. Here, we demonstrate a bright and polarized single-photon source operating in the telecom C-band based on an elliptical Bragg grating (EBG) cavity. With a significant Purcell enhancement of 5.25 ± 0.05, the device achieves a polarization ratio of 0.986, a single-photon purity of g 2 (0) = 0.078 ± 0.016, and a single-polarized photon collection efficiency of ∼24% at the first lens (NA = 0.65) without blinking. These findings suggest that C-band QD-based single-photon sources are potential candidates for advancing quantum communication.

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Telecom WavelengthsInP‐Based Quantum Dots for Quantum Communication

Benyoucef

Musiał

2023

Photonic Quantum Technologies

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Photonic Quantum Technologies

Cited by 4 publications

References 26 publications

Machine learning enhanced evaluation of semiconductor quantum dots

Machine learning enhanced evaluation of semiconductor quantum dots

Polarized and Bright Telecom C-Band Single-Photon Source from InP-Based Quantum Dots Coupled to Elliptical Bragg Gratings

Telecom WavelengthsInP‐Based Quantum Dots for Quantum Communication

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