Bulletin of the American Physical Society
APS March Meeting 2024
Monday–Friday, March 4–8, 2024; Minneapolis & Virtual
Session B49: Optically Active Spins - Dots, Silicon, and Diamond
11:30 AM–2:30 PM,
Monday, March 4, 2024
Room: 200G
Sponsoring
Units:
DQI GMAG
Chair: Arshag Danageozian, Virignia Tech
Abstract: B49.00005 : Mastering Quantum Emitter Analysis: Device Characterization in the Single-Photon Limit*
12:42 PM–12:54 PM
Presenter:
Markus Sifft
(Ruhr University Bochum)
Authors:
Markus Sifft
(Ruhr University Bochum)
Annika Kurzmann
(RWTH Achen)
Jens Kerski
(University of Duisburg-Essen)
Rüdiger Schott
(ETH Zürich)
Arne Ludwig
(Ruhr University Bochum)
Andreas D Wieck
(Ruhr University Bochum)
Axel Lorke
(University of Duisburg-Essen)
Paul M Geller
(University of Duisburg-Essen)
Daniel Hägele
(Ruhr University Bochum)
The emitter is characterized by determining the parameters of its Liouvillian or its transition matrix through a fitting procedure between theoretical polyspectra and those calculated directly from a detector output. Utilizing fluorescence data from a semiconductor quantum dot, we demonstrate that our approach can determine on- and off-switching rates at average photon levels far lower than typical system dynamics.
To complement our method, we have developed two Python libraries, SignalSnap and QuantumCatch, to readily implement these advanced methods. SignalSnap calculates detector output polyspectra, while QuantumCatch extracts system parameters from measured polyspectra [2,3]. Together, they offer a state-of-the-art toolset for quantum emitter analysis and parameter learning in Liouvillian and hidden Markov models that is applicable across a broad spectrum of scientific domains.
[1] Sifft et al., arXiv:2310.10464, [2] github.com/markussifft/signalsnap [3] github.com/markussifft/quantumcatch
*We acknowledge financial support by the German Science Foundation (DFG) under Project Nos. 341960391 and 510607185 (D.H.), 383065199 (Ar. L. and M.G.), 278162697 (Ax. L. and M. G. within SFB 1242) as well as by the Mercator Research Center Ruhr under Project No. Ko-2022-0013.
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