Proposal for witnessing non-classical light with the human eye

We give a complete proposal showing how to detect the non-classical nature of photonic states with naked eyes as detectors. The enabling technology is a sub-Poissonian photonic state that is obtained from single photons, displacement operations in phase space and basic non-photon-number-resolving detectors. We present a detailed statistical analysis of our proposal including imperfect photon creation and detection and a realistic model of the human eye. We conclude that a few tens of hours are sufficient to certify non-classical light with the human eye with a p-value of 10%.Comment: 9 pages, 5 figures, accepted versio

Comparing different approaches for generating random numbers device-independently using a photon pair source

What is the most efficient way to generate random numbers device-independently using a photon pair source based on spontaneous parametric down conversion (SPDC)? We consider this question by comparing two implementations of a detection-loophole-free Bell test. In particular, we study in detail a scenario where a heralded single photon source (HSPS) is used to herald path-entangled states, i.e. entanglement between two spatial modes sharing a single photon and where non-locality is revealed using photon counting preceded by small displacement operations. We start by giving a theoretical description of such a measurement. We then show how to optimize the Bell-CHSH violation through a non-perturbative calculation, taking the main experimental imperfections into account. We finally bound the amount of randomness that can be extracted and compare it to the one obtained with the conventional scenario using photon pairs entangled e.g. in polarization and analyzed through photon counting. While the former requires higher overall detection efficiencies, it is far more efficient in terms of both the entropy per experimental run and the rate of random bit generation.Comment: 12 pages, 5 figure

Dyson-Maleev representation of nonlinear sigma-models

For nonlinear sigma-models in the unitary symmetry class, the non-linear target space can be parameterized with cubic polynomials. This choice of coordinates has been known previously as the Dyson-Maleev parameterization for spin systems, and we show that it can be applied to a wide range of sigma-models. The practical use of this parameterization includes simplification of diagrammatic calculations (in perturbative methods) and of algebraic manipulations (in non-perturbative approaches). We illustrate the use and specific issues of the Dyson-Maleev parameterization with three examples: the Keldysh sigma-model for time-dependent random Hamiltonians, the supersymmetric sigma-model for random matrices, and the supersymmetric transfer-matrix technique for quasi-one-dimensional disordered wires. We demonstrate that nonlinear sigma-models of unitary-like symmetry classes C and B/D also admit the Dyson-Maleev parameterization.Comment: 16 pages, 1 figur

Topology Optimization of the Network with Renewable Energy Sources Generation Based on a Modified Adapted Genetic Algorithm

The article presents an adaptive genetic algorithm developed by the authors, which makes it possible to optimize the topology of a power network with distributed generation. The optimization was based on bioinspired methods. The objects of the study was a 15-node circuit of a power network with photovoltaic stations and a 14-node IEEE augmented circuit with distributed generation sources (three wind farms and two photovoltaic plants). The simulation of the modes of electric power systems was performed using the Pandapower library for the Python programming language, which is in the public domain. Three types of electric load of consumers were considered, reflecting the natures of electricity consumption in the nodes of real electric power systems, the results of numerical studies were presented. The proposed genetic algorithm used two different functions of interbreeding, the function of mutation, selection of the best individuals and mass mutation (complete population renewal). At the end of each iteration of the algorithm operation, statistical dependencies were derived that characterized its work: the best (minimal losses) and average adaptability in the population, a list of the best individuals throughout all iterations, etc. The verification was carried out in comparison with the results obtained by a complete search of possible radial configurations of the system, and it showed that the developed genetic algorithm had fast convergence, high accuracy and was able to work correctly with different configurations of electrical circuits, generation and load structures. The algorithm can be used in conjunction with renewable energy sources generation forecasting systems for the day ahead when planning the operating modes of power units in order to minimize the costs of covering electricity losses and improve the quality of electricity supplied. © 2022 Belarusian National Technical University. All rights reserved

Operational Forecasting of Wind Speed for an Self-Contained Power Assembly of a Traction Substation

Currently, the prospects of creating hybrid power assemblies using renewable energy sources, including wind energy, and energy storage systems based on hydrogen energy technologies are being considered. To control such an energy storage system, it is necessary to perform operational renewable sources generation forecasting, particularly forecasting of wind power assemblies. Their production depends on the speed and direction of the wind. The article presents the results of solving the problem of operational forecasting of wind speed for a hybrid power assembly project aimed at increasing the capacity of the railway section between Yaya and Izhmorskaya stations (Kemerovo region of the Russian Federation). Hourly data of wind speeds and directions for 15 years have been analyzed, a neural network model has been built, and a compact architecture of a multilayer perceptron has been proposed for short-term forecasting of wind speed and direction for 1 and 6 hours ahead. The model that has been developed allows minimizing the risks of overfitting and loss of forecasting accuracy due to changes in the operating conditions of the model over time. A specific feature of this work is the stability investigation of the model trained on the data of long-term observations to long-term changes, as well as the analysis of the possibilities of improving the accuracy of forecasting due to regular further training of the model on newly available data. The nature of the influence of the size of the training sample and the self-adaptation of the model on the accuracy of forecasting and the stability of its work on the horizon of several years has been established. It is shown that in order to ensure high accuracy and stability of the neural network model of wind speed forecasting, long-term meteorological observations data are required. © Belarusian National Technical University, 2023

Improving of the Generation Accuracy Forecasting of Photovoltaic Plants Based on k-Means and k-Nearest Neighbors Algorithms

Renewable energy sources (RES) are seen as a means of the fuel and energy complex carbon footprint reduction but the stochastic nature of generation complicates RES integration with electric power systems. Therefore, it is necessary to develop and improve methods for forecasting of the power plants generation using the energy of the sun, wind and water flows. One of the ways to improve the accuracy of forecast models is a deep analysis of meteorological conditions as the main factor affecting the power generation. In this paper, a method for adapting of forecast models to the meteorological conditions of photovoltaic stations operation based on machine learning algorithms was proposed and studied. In this case, unsupervised learning is first performed using the k-means method to form clusters. For this, it is also proposed to use studied the feature space dimensionality reduction algorithm to visualize and estimate the clustering accuracy. Then, for each cluster, its own machine learning model was trained for generation forecasting and the k-nearest neighbours algorithm was built to attribute the current conditions at the model operation stage to one of the formed clusters. The study was conducted on hourly meteorological data for the period from 1985 to 2021. A feature of the approach is the clustering of weather conditions on hourly rather than daily intervals. As a result, the mean absolute percentage error of forecasting is reduced significantly, depending on the prediction model used. For the best case, the error in forecasting of a photovoltaic plant generation an hour ahead was 9 %. © 2023 Belarusian National Technical University. All rights reserved

Entanglement of trapped-ion qubits separated by 230 meters

We report on an elementary quantum network of two atomic ions separated by 230 m. The ions are trapped in different buildings and connected with 520(2) m of optical fiber. At each network node, the electronic state of an ion is entangled with the polarization state of a single cavity photon; subsequent to interference of the photons at a beamsplitter, photon detection heralds entanglement between the two ions. Fidelities of up to $(88.2+2.3-6.0)\%$ are achieved with respect to a maximally entangled Bell state, with a success probability of $4 \times 10^{-5}$. We analyze the routes to improve these metrics, paving the way for long-distance networks of entangled quantum processors

Оперативное прогнозирование скорости ветра для автономной энергетической установки тяговой железнодорожной подстанции

Currently, the prospects of creating hybrid power assemblies using renewable energy sources, including wind energy, and energy storage systems based on hydrogen energy technologies are being considered. To control such an energy storage system, it is necessary to perform operational renewable sources generation forecasting, particularly forecasting of wind power assemblies. Their production depends on the speed and direction of the wind. The article presents the results of solving the problem of operational forecasting of wind speed for a hybrid power assembly project aimed at increasing the capacity of the railway section between Yaya and Izhmorskaya stations (Kemerovo region of the Russian Federation). Hourly data of wind speeds and directions for 15 years have been analyzed, a neural network model has been built, and a compact architecture of a multilayer perceptron has been proposed for short-term forecasting of wind speed and direction for 1 and 6 hours ahead. The model that has been developed allows minimizing the risks of overfitting and loss of forecasting accuracy due to changes in the operating conditions of the model over time. A specific feature of this work is the stability investigation of the model trained on the data of long-term observations to long-term changes, as well as the analysis of the possibilities of improving the accuracy of forecasting due to regular further training of the model on newly available data. The nature of the influence of the size of the training sample and the self-adaptation of the model on the accuracy of forecasting and the stability of its work on the horizon of several years has been established. It is shown that in order to ensure high accuracy and stability of the neural network model of wind speed forecasting, long-term meteorological observations data are required.В настоящее время рассматриваются перспективы создания гибридных энергетических установок с использованием возобновляемых источников энергии, в том числе энергии ветра, и систем накопления энергии на базе технологий водородной энергетики. Для управления такой системой накопления энергии необходимо оперативное прогнозирование генерации от возобновляемых источников, в частности ветровых энергетических установок. Их выработка зависит от скорости и направления ветра. В статье представлены результаты решения задачи оперативного прогнозирования скорости ветра для проекта гибридной энергетической установки, направленной на повышение пропускной способности железнодорожного участка между станциями Яя и Ижморская (Кемеровская область Российской Федерации). Проанализированы почасовые данные скоростей и направлений ветра за 15 лет, построена нейросетевая модель и предложена компактная архитектура многослойного перцептрона для краткосрочного прогнозирования скорости и направления ветра на 1 и 6 ч вперед. Разработанная модель позволяет минимизировать риски переобучения и потери точности прогнозирования из-за изменения условий работы модели со временем. Особенность данной статьи заключается в исследовании устойчивости модели, обученной на данных многолетних наблюдений, к долгосрочным изменениям, а также анализе возможностей повышения точности прогнозирования за счет регулярного дообучения модели на вновь поступающих данных. Установлен характер влияния размера обучающей выборки и самоадаптации модели на точность прогнозирования и устойчивость ее работы на горизонте в несколько лет. Показано, что для обеспечения высокой точности и устойчивости нейросетевой модели прогнозирования скорости ветра необходимы данные многолетних метеорологических наблюдений

Оптимизация топологии сети с ВИЭ-генерацией на основе модифицированного адаптированного генетического алгоритма

The article presents an adaptive genetic algorithm developed by the authors, which makes it possible to optimize the topology of a power network with distributed generation. The optimization was based on bioinspired methods. The objects of the study were a 15-node circuit of a power net-work with photovoltaic stations and a 14-node IEEE augmented circuit with distributed generation sources (three wind farms and two photovoltaic plants). The simulation of the modes of electric power systems was performed using the Pandapower library for the Python programming language, which is in the public domain. Three types of electric load of consumers were considered, reflecting the natures of electricity consumption in the nodes of real electric power systems, the results of numerical studies were presented. The proposed genetic algorithm used two different functions of interbreeding, the function of mutation, selection of the best individuals and mass mutation (complete population renewal). At the end of each iteration of the algorithm operation, statistical dependencies were de-rived that characterized its work: the best (minimal losses) and average adaptability in the population, a list of the best individuals throughout all iterations, etc. The verification was carried out in comparison with the results obtained by a complete search of possible radial configurations of the system, and it showed that the developed genetic algorithm had fast convergence, high accuracy and was able to work correctly with different configurations of electrical circuits, generation and load structures. The algorithm can be used in conjunction with renewable energy sources generation forecasting systems for the day ahead when planning the operating modes of power units in order to minimize the costs of covering electricity losses and improve the quality of electricity supplied.В статье представлен разработанный авторами адаптивный генетический алгоритм, позволяющий оптимизировать топологию электрической сети с распределенной генерацией на основе биоинспирированных методов. Объекты исследования – 15-узловая схема электрической сети с фотоэлектрическими станциями и 14-узловая дополненная схема IEEE с источниками распределенной генерации (три ветровые и две фотоэлектрические станции). Моделирование режимов электроэнергетических систем выполнено с использованием находящейся в открытом доступе библиотеки Pandapower для языка программирования Python. Рассмотрены три типа электрической нагрузки потребителей, отражающие характер потребления электроэнергии в узлах реальных электроэнергетических систем, приведены результаты численных исследований. В предложенном генетическом алгоритме применены две различные функции скрещивания, функции мутации, отбора лучших индивидов и массовой мутации (полного обновления популяции). В конце каждой итерации работы алгоритма выводятся статистические зависимости, характеризующие его работу: лучшая (минимальные потери) и средняя приспособленность в популяции, список лучших индивидов на протяжении всех итераций и т. д. Верификация производилась в сравнении с результатами, полученными методом полного перебора возможных радиальных конфигураций системы, и показала, что разработанный генетический алгоритм обладает быстрой сходимостью, высокой точностью и способен корректно работать при различных конфигурациях схем электрических сетей, структурах генерации и нагрузки. Алгоритм может применяться совместно с системами прогнозирования ВИЭ-генерации на сутки вперед при планировании режимов работы энергообъединений с целью минимизации издержек на покрытие потерь электроэнергии и улучшения качества отпускаемой электроэнергии

Повышение точности прогнозирования генерации фотоэлектрических станций на основе алгоритмов k-средних и k-ближайших соседей

Renewable energy sources (RES) are seen as a means of the fuel and energy complex carbon footprint reduction but the stochastic nature of generation complicates RES integration with electric power systems. Therefore, it is necessary to develop and improve methods for forecasting of the power plants generation using the energy of the sun, wind and water flows. One of the ways to improve the accuracy of forecast models is a deep analysis of meteorological conditions as the main factor affecting the power generation. In this paper, a method for adapting of forecast models to the meteorological conditions of photovoltaic stations operation based on machine learning algorithms was proposed and studied. In this case, unsupervised learning is first performed using the k-means method to form clusters. For this, it is also proposed to use studied the feature space dimensionality reduction algorithm to visualize and estimate the clustering accuracy. Then, for each cluster, its own machine learning model was trained for generation forecasting and the k-nearest neighbours algorithm was built to attribute the current conditions at the model operation stage to one of the formed clusters. The study was conducted on hourly meteorological data for the period from 1985 to 2021. A feature of the approach is the clustering of weather conditions on hourly rather than daily intervals. As a result, the mean absolute percentage error of forecasting is reduced significantly, depending on the prediction model used. For the best case, the error in forecasting of a photovoltaic plant generation an hour ahead was 9 %.Возобновляемые источники энергии рассматриваются как средство снижения углеродного следа топливно-энергетического комплекса, при этом стохастический характер генерации осложняет их интеграцию с электроэнергетическими системами. Эта существенная трудность обусловливает необходимость создавать и совершенствовать методы прогнозирования генерации электрических станций, использующих энергию солнца, ветра и водных потоков. Наиболее важным направлением, обеспечивающим повышение точности прогнозных моделей, является глубокий анализ метеорологических условий как главного фактора, влияющего на выработку электроэнергии. В данной работе предложен и исследован метод адаптации прогнозных моделей под метеорологические условия работы фотоэлектрических станций на базе алгоритмов машинного обучения. При этом вначале выполняется обучение без учителя методом k-средних для формирования кластеров. Для этой задачи также предложено и исследовано использование алгоритма понижения размерности пространства признаков для визуализации оценки точности кластеризации. Затем для каждого кластера построена своя модель машинного обучения для формирования прогнозов и алгоритм k-ближайших соседей для отнесения текущих условий на этапе эксплуатации модели к одному из сформированных кластеров. Исследование было проведено на почасовых метеорологических данных за период с 1985 по 2021 г. Одной из особенностей этого подхода является кластеризация метеоусловий на часовых, а не суточных интервалах. В результате средний модуль относительной ошибки прогнозирования существенно снижается в зависимости от используемой модели прогнозирования. Для наилучшего варианта ошибка прогнозирования генерации фотоэлектрической станции на час вперед составила 9 %