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THU: Thursday Contributed Sessions

THU 13: Poster Session: Applications

THU 13.63: Poster

Donnerstag, 11. September 2025, 16:30–18:30, ZHG Foyer 1. OG

Deep Learning Strategies for Stabilizing NV Center Emission Spectra — •Clara Zoé Baenz¹, Gregor Pieplow¹, Kilian Unterguggenberger¹, Laura Orphal-Kobin¹, and Tim Schröder^1,2 — ¹Humboldt-Universität zu Berlin, Berlin, Germany — ²Ferdinand-Braun-Institut, Berlin, Germany

Understanding and predicting spectral diffusion is essential for stabilizing quantum emitters in photonic networks. We explore the use of machine learning, specifically recurrent neural networks with Long Short-Term Memory (LSTM) architecture, to model and forecast spectral fluctuations in nitrogen vacancies in diamond. By training LSTM networks on time-series data of the optical transition frequency, we aim to perform corrections that can be predicted to stabilize the zero-phonon line of a quantum emitter. We present a preliminary test of our model with negatively charged nitrogen vacancies in nano pillars.