Quantum 2025 – scientific programme
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THU: Thursday Contributed Sessions
THU 13: Poster Session: Applications
THU 13.63: Poster
Thursday, September 11, 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 Schröder1,2 — 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.