Mastering Quantum Emitter Analysis: Device Characterization in the Single-Photon Limit

Traditional methods, such as full counting statistics and the Viterbi algorithm, often fall short in accurately analyzing emitter behaviors, especially in low-photon-rate regimes. To address this, we introduce a polyspectra (higher-order generalizations of the usual power spectrum) approach that is effective even in scenarios of high photon loss or low photon rates by eliminating the need for both a minimum photon flux and conventional photon-event binning [1].

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