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The development of prototype metropolitan-scale quantum networks is underway and entails transmitting quantum information via single photons through deployed optical fibers spanning several tens of kilometers. The major challenges in building metropolitan-scale quantum networks are compensation of polarization mode dispersion, high-precision clock synchronization, and compensation for cumulative transmission time fluctuations. One approach addressing these challenges is to co-propagate classical probe signals in the same fiber as the quantum signal. Thus, both signals experience the same conditions, and the changes of the fiber can therefore be monitored and compensated. Here, we demonstrate the distribution of polarization entangled quantum signals co-propagating with the White Rabbit Precision Time Protocol (WR-PTP) classical signals in the same single-core fiber strand at metropolitan-scale distances. Our results demonstrate the feasibility of this quantum-classical coexistence by achieving high-fidelity entanglement distribution between nodes separated by 100 km of optical fiber. This advancement is a significant step towards the practical implementation of robust and efficient metropolitan-scale quantum networks.

Imaging Atmospheric Cherenkov Telescopes (IACTs) of gamma ray observatory TAIGA detect the Extesnive Air Showers (EASs) originating from the cosmic or gamma rays interactions with the atmosphere. Thereby, telescopes obtain images of the EASs. The ability to segregate gamma rays images from the hadronic cosmic ray background is one of the main features of this type of detectors. However, in actual IACT observations simultaneous observation of the background and the source of gamma ray is needed. This observation mode (called wobbling) modifies images of events, which affects the quality of selection by neural networks. Thus, in this work, the results of the application of neural networks (NN) for image classification task on Monte Carlo (MC) images of TAIGA-IACTs are presented. The wobbling mode is considered together with the image adaptation for adequate analysis by NNs. Simultaneously, we explore several neural network structures that classify events both directly from images or through Hillas parameters extracted from images. In addition, by employing NNs, MC simulation data are used to evaluate the quality of the segregation of rare gamma events with the account of all necessary image modifications., Comment: Version of article, submitted to journal

Modern detectors of cosmic gamma-rays are a special type of imaging telescopes (air Cherenkov telescopes) supplied with cameras with a relatively large number of photomultiplier-based pixels. For example, the camera of the TAIGA-IACT telescope has 560 pixels of hexagonal structure. Images in such cameras can be analysed by deep learning techniques to extract numerous physical and geometrical parameters and/or for incoming particle identification. The most powerful deep learning technique for image analysis, the so-called convolutional neural network (CNN), was implemented in this study. Two open source libraries for machine learning, PyTorch and TensorFlow, were tested as possible software platforms for particle identification in imaging air Cherenkov telescopes. Monte Carlo simulation was performed to analyse images of gamma-rays and background particles (protons) as well as estimate identification accuracy. Further steps of implementation and improvement of this technique are discussed., Comment: 5 pages, 2 figures. Submitted to CEUR Workshop Proceedings, 8th International Conference "Distributed Computing and Grid-technologies in Science and Education" GRID 2018, 10 - 14 September 2018, Dubna, Russia

The paper is devoted to the issues of raw binary data documenting, parsing and verifying in astroparticle data lifecycle. The long-term preservation of raw data of astroparticle experiments as originally generated is essential for re-running analyses and reproducing research results. The selected high-quality raw data should have detailed documentation and accompanied by open software tools for access to them. We consider applicability of binary file format description languages to specify, parse and verify raw data of the Tunka Advanced Instrument for cosmic rays and Gamma Astronomy (TAIGA) experiment. The formal specifications are implemented for five data formats of the experiment and provide automatic generation of source code for data reading libraries in target programming languages (e.g. C++, Java, and Python). These libraries were tested on TAIGA data. They showed a good performance and help us to locate the parts with corrupted data. The format specifications can be used as metadata for exchanging of astroparticle raw data. They can also simplify software development for data aggregation from various sources for the multi-messenger analysis.

In this work we compare two open source machine learning libraries, PyTorch and TensorFlow, as software platforms for rejecting hadron background events detected by imaging air Cherenkov telescopes (IACTs). Monte Carlo simulation for the TAIGA-IACT telescope is used to estimate background rejection quality. A wide variety of neural network algorithms provided by both libraries can easily be tested on various types of data, which is useful for various imaging air Cherenkov experiments. The work is a component of the Astroparticle.online project, which collaborates with the TAIGA and KASCADE experiments and welcomes any astroparticle experiment to join., Comment: 6 pages, 2 figures. Submitted to JPCS, 26th Extended European Cosmic Ray Symposium and 35th Russian Cosmic Ray Conference (E+CRS 2018 / RCRC 2018), Barnaul - Belokurikha, July 6 - 10, 2018

Quantum mechanics allows measurements that surpass the fundamental sensitivity limits of classical methods. To benefit from the quantum advantage in a practical setting, the receiver should use communication channels resources optimally; this can be done employing large communication alphabets. Here we show the fundamental sensitivity potential of a quantum receiver for coherent communication with frequency shift keying. We introduce an adaptive quantum protocol for this receiver, show that its sensitivity outperforms other receivers for alphabet sizes above 4 and scales favorably, whereas quantum receivers explored to date suffer from degraded sensitivity with the alphabet size. In addition, we show that the quantum measurement advantage allows the much better use of the frequency space in comparison to classical frequency keying protocols and orthogonal frequency division multiplexing.

We present a method to reconstruct the complete statistical mode structure and optical losses of multimode conjugated optical fields using an experimentally measured joint photon-number probability distribution. We demonstrate that this method evaluates classical and non-classical properties using a single measurement technique and is well-suited for quantum mesoscopic state characterization. We obtain a nearly-perfect reconstruction of a field comprised of up to 10 modes based on a minimal set of assumptions. To show the utility of this method, we use it to reconstruct the mode structure of an unknown bright parametric down-conversion source., Comment: 7 pages, 3 figures

We study the processes in a quantum dot and its surrounds under resonant excitation with the addition of weak non-resonant light. We observe a decrease in inhomogeneous emission linewidth, as well as previously observed enhancement of resonant fluorescence (RF). Scanning excitation and detection frequencies, we distinguish between homogeneous and inhomogeneous broadening. Monte Carlo simulations model the enhanced RF only if we include charge-carrier loss from the quantum dot induced by the resonant laser. The cause of the linewidth narrowing is predominantly independent of the enhanced RF.

Here we present a simple and robust method to build on-the-fly configurable quantum gates based on a photonic exchange between quantum nodes. The idea is based on a high reflectivity of Bragg grating structures near resonant wavelengths. The control is exerted by applying an external strongly off-resonant or even a static electromagnetic field and taking advantage of the Kerr effect. When the nonlinear phase shift is strong enough, the Bragg mirror disappears, thereby allowing a transmission of a wave packet from one node to another. An example of a protocol for quantum logic gates that relies on this framework is offered., Comment: 8 pages, 5 figures

Here we present a reconstruction of the Positive Operator-Value Measurement of a photon-number-resolving detector comprised of three 50:50 beamsplitters in a tree configuration, terminated with four single-photon avalanche detectors. The four detectors' outputs are processed by an electronic board that discriminates detected photon number states from 0 to 4 and implements a "smart counting" routine to compensate for dead time issues at high count rates.

A double-crystal diamond (111) monochromator recently implemented at the Linac Coherent Light Source (LCLS) enables splitting of the primary X-ray beam into a pink (transmitted) and a monochromatic (reflected) branch. The first monochromator crystal with a thickness of 100 um provides sufficient X-ray transmittance to enable simultaneous operation of two beamlines. Here we report on the design, fabrication, and X-ray characterization of the first and second (300-um-thick) crystals utilized in the monochromator and the optical assemblies holding these crystals. Each crystal plate has a region of about 5 X 2 mm2 with low defect concentration, sufficient for use in X-ray optics at the LCLS. The optical assemblies holding the crystals were designed to provide mounting on a rigid substrate and to minimize mounting-induced crystal strain. The induced strain was evaluated using double-crystal X-ray topography and was found to be small over the 5 X 2 mm2 working regions of the crystals.