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Register a new userRapid classification of quantum sources enabled by machine learning
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Deterministic nanoassembly may enable unique integrated on-chip quantum photonic devices. Such integration requires a careful large-scale selection of nanoscale building blocks such as solid-state single-photon emitters by the means of optical characterization. Second-order autocorrelation is a cornerstone measurement that is particularly time-consuming to realize on a large scale. We have implemented supervised machine learning-based classification of quantum emitters as single or not-single based on their sparse autocorrelation data. Our method yields a classification accuracy of over 90% within an integration time of less than a second, realizing roughly a hundredfold speedup compared to the conventional, Levenberg-Marquardt approach. We anticipate that machine learning-based classification will provide a unique route to enable rapid and scalable assembly of quantum nanophotonic devices and can be directly extended to other quantum optical measurements.
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The ESAs X-ray Multi-Mirror Mission (XMM-Newton) created a new, high quality version of the XMM-Newton serendipitous source catalogue, 4XMM-DR9, which provides a wealth of information for observed sources. The 4XMM-DR9 catalogue is correlated with the Sloan Digital Sky Survey (SDSS) DR12 photometric database and the ALLWISE database, then we get the X-ray sources with information from X-ray, optical and/or infrared bands, and obtain the XMM-WISE sample, the XMM-SDSS sample and the XMM-WISE-SDSS sample. Based on the large spectroscopic surveys of SDSS and the Large Sky Area Multi-object Fiber Spectroscopic Telescope (LAMOST), we cross-match the XMM-WISE-SDSS sample with those sources of known spectral classes, and obtain the known samples of stars, galaxies and quasars. The distribution of stars, galaxies and quasars as well as all spectral classes of stars in 2-d parameter spaces is presented. Various machine learning methods are applied on different samples from different bands. The better classified results are retained. For the sample from X-ray band, rotation forest classifier performs the best. For the sample from X-ray and infrared bands, a random forest algorithm outperforms all other methods. For the samples from X-ray, optical and/or infrared bands, LogitBoost classifier shows its superiority. Thus, all X-ray sources in the 4XMM-DR9 catalogue with different input patterns are classified by their respective models which are created by these best methods. Their membership and membership probabilities to individual X-ray sources are assigned. The classified result will be of great value for the further research of X-ray sources in greater detail.
Manipulation of light-induced magnetization has become a fundamentally hot topic with a potentially high impact for atom trapping, confocal and magnetic resonance microscopy, and data storage. However, the control of the magnetization orientation mainly relies on the direct methods composed of amplitude, phase and polarization modulations of the incident light under the tight focusing condition, leaving arbitrary three-dimensional (3D) magnetization orientation completely inaccessible. Here, we propose a facile approach called machine learning inverse design to achieve expected vectorial magnetization orientation. This pathway is timeefficient and accurate to produce the demanded incident beam for arbitrary prescribed 3D magnetization orientation. We are confident to believe that the machine learning method is not only applied for magnetization orientations, but also widely used in the control of magnetization structures.
In general, the typical approach to discriminate antibunching, bunching or superbunching categories make use of calculating the second-order coherence function ${g^{(2)}}(tau )$ of light. Although the classical light sources correspond to the specific degree of second-order coherence ${g^{(2)}}(0)$, it does not alone constitute a distinguishable metric to characterize and determine light sources. Here we propose a new mechanism to directly classify and generate antibunching, bunching or superbunching categories of light, as well as the classical light sources such as thermal and coherent light, by Gamma fitting according to only one characteristic parameter $alpha$ or $beta$. Experimental verification of beams from four-wave mixing process is in agreement with the presented mechanism, and the in fluence of temperature $T$ and laser detuning $Delta$ on the measured results are investigated. The proposal demonstrates the potential of classifying and identifying light with different nature, and the most importantly, provides a convenient and simple method to generate light sources meeting various application requirements according to the presented rules. Most notably, the bunching and superbunching are distinguishable in super-Poissonian statistics using our mechanism.
We propose the concept of one-sided quantum interference based on non-Hermitian metasurfaces.By designing bianisotropic metasurfaces with a non-Hermitian exceptional point, we show that quantum interference can exist only on only one side but not another. This is the quantum inheritance of unidirectional zero reflection in classical optics.The one-side interference can be further manipulated with tailor-made metasurface. With two photons simultaneously entering the metasurface from different sides, the probability for only outputting one photon on the side with reflection can be modified to zero as a one-sided destructive quantum interference while the output on another side is free of interference. We design the required bianisotropic metasurface and numerically demonstrate the proposed effect. The non-Hermitian bianisotropic metasurfaces provide more degrees of freedom in tuning two-photon quantum interference, in parallel to the celebrated Hong-Ou-Mandel effect.
Improving axial resolution is crucial for three-dimensional optical imaging systems. Here we present a scheme of axial superresolution for two incoherent point sources based on spatial mode demultiplexing. A radial mode sorter is used to losslessly decompose the optical fields into a radial mode basis set to extract the phase information associated with the axial positions of the point sources. We show theoretically and experimentally that, in the limit of a zero axial separation, our scheme allows for reaching the quantum Cramer-Rao lower bound and thus can be considered as one of the optimal measurement methods. Unlike other superresolution schemes, this scheme does not require neither activation of fluorophores nor sophisticated stabilization control. Moreover, it is applicable to the localization of a single point source in the axial direction. Our demonstration can be useful to a variety of applications such as far-field fluorescence microscopy.
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