Identification of light sources using machine learning

Abstract

The identification of light sources represents a task of utmost importance for the development of multiple photonic technologies. Over the last decades, the identification of light sources as diverse as sunlight, laser radiation, and molecule fluorescence has relied on the collection of photon statistics or the implementation of quantum state tomography. In general, this task requires an extensive number of measurements to unveil the characteristic statistical fluctuations and correlation properties of light, particularly in the low-photon flux regime. In this article, we exploit the self-learning features of artificial neural networks and the naive Bayes classifier to dramatically reduce the number of measurements required to discriminate thermal light from coherent light at the single-photon level. We demonstrate robust light identification with tens of measurements at mean photon numbers below one. In terms of accuracy and number of measurements, the methods described here dramatically outperform conventional schemes for characterization of light sources. Our work has important implications for multiple photonic technologies such as light detection and ranging, and microscopy.

Photons detected in the active nerve by photographic technique

“…In literature, an interesting method for photon emission detection which makes use of superconducting nanowire single-photon detector (SNSPD) is described. In particular, this can be found in a recently published work 36 from Chenglong You et al, which seems a very interesting approach, exploiting the advantages of self-learning features of artificial neural networks and the naive Bayes classifier in the photon emission detection and offering new possibilities in terms of studying extremely weak light sources.…”

Rapid Classification of Quantum Sources Enabled by Machine Learning

“…[22][23][24][25][26][27] Recently, ML algorithms have also been applied to quantum photonics. [28][29][30][31][32][33][34] Combining the Bayesian phase estimation with Hamiltonian Learning techniques for analyzing large datasets www.advancedsciencenews.com www.advquantumtech.com from nitrogen vacancy (NV) centers in bulk diamond allowed for magnetic field measurements with extreme sensitivity at room temperature. [35] Hamiltonian Learning was adopted for the characterization of different quantum systems, [36] including the characterization of electron spin states in diamond NV centers.…”

Neural-network approach for identifying nonclassicality from click-counting data

“…In recent years, the methods of machine learning have been widely applied by the quantum science community [31]. As a general tool for optimization problems, machine learning was used in the study of quantum tomography [32][33][34][35] and quantum state discrimination [36,37], quantum metrology [38][39][40], quantum error correction [41][42][43][44], quantum manybody systems [45][46][47], quantum state and gate preparation [48][49][50][51][52], and certification of quantum dynamics [53,54], to mention just a few. Also in the task of identifying nonlocal correlations, machine learning was shown to prove advantageous [55][56][57].…”