Predicting quantum emitter fluctuations with time-series forecasting models

dc.contributor.authorRamezani, Fereshteh
dc.contributor.authorStrasbourg, Matthew
dc.contributor.authorParvez, Sheikh
dc.contributor.authorSaxena, Ravindra
dc.contributor.authorJariwala, Deep
dc.contributor.authorBorys, Nicholas J.
dc.contributor.authorWhitaker, Bradley M.
dc.date.accessioned2024-06-10T18:56:12Z
dc.date.available2024-06-10T18:56:12Z
dc.date.issued2024-03
dc.description.abstract2D materials have important fundamental properties allowing for their use in many potential applications, including quantum computing. Various Van der Waals materials, including Tungsten disulfide (WS2), have been employed to showcase attractive device applications such as light emitting diodes, lasers and optical modulators. To maximize the utility and value of integrated quantum photonics, the wavelength, polarization and intensity of the photons from a quantum emission (QE) must be stable. However, random variation of emission energy, caused by the inhomogeneity in the local environment, is a major challenge for all solid-state single photon emitters. In this work, we assess the random nature of the quantum fluctuations, and we present time series forecasting deep learning models to analyse and predict QE fluctuations for the first time. Our trained models can roughly follow the actual trend of the data and, under certain data processing conditions, can predict peaks and dips of the fluctuations. The ability to anticipate these fluctuations will allow physicists to harness quantum fluctuation characteristics to develop novel scientific advances in quantum computing that will greatly benefit quantum technologies.
dc.identifier.citationRamezani, F., Strasbourg, M., Parvez, S. et al. Predicting quantum emitter fluctuations with time-series forecasting models. Sci Rep 14, 6920 (2024). https://doi.org/10.1038/s41598-024-56517-0
dc.identifier.doi10.1038/s41598-024-56517-0
dc.identifier.issn2045-2322
dc.identifier.urihttps://scholarworks.montana.edu/handle/1/18611
dc.language.isoen_US
dc.publisherSpringer Science and Business Media LLC
dc.subjectQuantum emitter
dc.subjectQuantum emission
dc.subjectFluctuations
dc.subjectForecast
dc.subjectTime-Series
dc.subjectLSTM
dc.subjectPrediction
dc.subjectDeep learning
dc.subjectNeural Network
dc.subjectRecurrent Neural Network
dc.titlePredicting quantum emitter fluctuations with time-series forecasting models
dc.typeArticle

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