Freiburg 2024 – wissenschaftliches Programm

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Q: Fachverband Quantenoptik und Photonik

Q 37: Poster III

Q 37.58: Poster

Mittwoch, 13. März 2024, 17:00–19:00, Tent B

Machine learning improved search for nitrogen-vacancy colour centres with long coherence times — •Ricky-Joe Plate, Jan Thieme, and Kilian Singer — Universität Kassel, Kassel, Germany

Nitrogen-vacancy colour centres are offering promising qubits for room temperature quantum information processing [1]. The quality of the qubits varies over a typical diamond sample and finding colour centres with long coherence times can be a time-consuming process in the lab. Here we present the architecture of a machine learning-based network [2] that allows for an automated search and characterization of optimal colour centres. An open-source implementation based on high-speed c++ code will be presented, that allows easy integration of custom improvements to the code base.

[1]: Maurer, P.C., Kucsko, G., Latta, C. (2012): Room-Temperature Quantum Bit Memory Exceeding One Second, in: Science Vol 336 Issue 6086, pp. 1283-1286, doi: 10.1126/science.1220513. [2]: Jiang, X., Hadid, A., Pang, Y., Granger, E. und Feng, X. (Hrsg.) (2019) Deep learning in object detection and recognition. Singapore: Springer.