Sequences of the ranged amplitudes as a universal method for fast noninvasive characterization of SPAD dark counts

Abstract

Single-photon detectors based on avalanche photodiodes (SPADs) are key elements of many modern highly sensitive optical systems. One of the bottlenecks of such detectors is an afterpulsing effect, which limits detection rate and requires an optimal hold-off time. In this paper, we propose a novel approach for statistical analysis of SPAD dark counts, and we demonstrate its usefulness for the search of the experimental condition where the afterpulsing effect can be strongly eliminated. This approach exploits a sequence of ranked time intervals between the dark counts and does not contain a complex mathematical analysis of the experimental data. We show that the approach can be efficiently applied for a small number of the dark counts, and it seems to be very beneficial for practical fast characterization of SPAD devices.

Robust diagnostics of dark counts for quantum networks

“…To search for an analytical expression, it is convenient to use an approximate expression connecting the SRA with the empirical distribution function F(x) [6,7]:…”

Correlation Defense for Quantum Randomness

“…Note that the properties of these four subsamples are homogeneous with respect to time, and two former (1, 2) and latter (3,4) obtained on the prototype of the quantum random number generator on homodyne detection. We discovered a local regression to the challenge of sustainable criteria of significance for nonrandomness in the source sample.…”

“…According to this definition, the sequence of SRA {x n } is composed of exactly the same elements as the original sequence {X k }; therefore, SRA is a noninvasive (without loss of information) statistical quantitative characteristic of a data sample [10]. SRA is related to the distribution function by the following approximate relation (where N is the sample size) [4,5,11,12]:…”

“…We discovered a local regression to the challenge of sustainable criteria of significance for nonrandomness in the source sample. The problem may be solved on the basis of the methods of SRAidentification [3] statistics, which, as we have shown earlier, can be effectively applied even to the short samples of quantum data [4,5].…”

“…In addition, experimental physics has no ideal measuring technique capable of directly determining the "ideal" quantum randomness. Therefore, it is necessary to develop stable methods for nonparametric analysis of quantum time series [4,5] in order to elaborate the fundamental criteria for the quantitative parameterization of quantum randomness.…”