Predicting quantum emitter fluctuations with time-series forecasting models


2D 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.



Quantum emitter, Quantum emission, Fluctuations, Forecast, Time-Series, LSTM, Prediction, Deep learning, Neural Network, Recurrent Neural Network


Ramezani, F., Strasbourg, M., Parvez, S. et al. Predicting quantum emitter fluctuations with time-series forecasting models. Sci Rep 14, 6920 (2024).
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