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The blinking statistics of quantum emitters and their corresponding Markovmodels play an important role in high resolution microscopy of biologicalsamples as well as in nano-optoelectronics and many other fields of science andengineering. Current methods for analyzing the blinking statistics like thefull counting statistics or the Viterbi algorithm break down for low photonrates. We present an evaluation scheme that eliminates the need for both aminimum photon flux and the usual binning of photon events which limits themeasurement bandwidth. Our approach is based on higher order spectra of themeasurement record which we model within the recently introduced method ofquantum polyspectra from the theory of continuous quantum measurements. Byvirtue of this approach we can determine on- and off-switching rates of asemiconductor quantum dot at light levels 1000 times lower than in a standardexperiment and 20 times lower than achieved with a scheme from full countingstatistics. Thus a very powerful high-bandwidth approach to the parameterlearning task of single photon hidden Markov models has been established withapplications in many fields of science.

Making Every Photon Count: A Quantum Polyspectra Approach to the Dynamics of Blinking Quantum Emitters at Low Photon Rates Without Binning