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High-dimensional quantum states have already settled their advantages in different quantum technology applications. However, their reliable transmission in fiber links remains an open challenge that must be addressed to boost their application, e.g. in the future quantum internet. Here, we prove how path encoded high-dimensional quantum states can be reliably transmitted over a 2 km long multicore fiber, taking advantage of a phase-locked loop system guaranteeing a stable interferometric detection.Index Terms-Quantum communication, quantum key distribution, high-dimensional path encoding, multicore fiber

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The k-out-of-n system model is widely applied for the reliability evaluation of many technical systems. Multi-state system modelling is also widely used for representing real systems, whose components can have different levels of performance. For these researches, recently multistate k-out-of-n systems have been comprehensively studied. In these studies, it is usually assumed that the system has a single task function to complete in a given environment. Moreover, the system or component performance is characterised by one measure, for example "electric power" in generation systems or "flow-rate" in transmission systems. However, this can be a simplification for some real-life engineering systems. For example, an intertwined district heating and electricity system consists of combined heat and power generating units, which can produce both electricity and heat. In this paper, definitions of multi-performance weighted multi-state components are provided and two generalized multi-performance multi-state K¯-out-of-n system models are proposed. Universal generating function approach is developed for the evaluation of such systems, with two numerical examples.

Abstract-The electrical demand forecasting problem can be regarded as a nonlinear time series prediction problem depending on many complex factors since it is required at various aggregation levels and at high temporal resolution. To solve this challenging problem, various time series and machine learning approaches have been proposed in the literature. As an evolution of neural network-based prediction methods, deep learning techniques are expected to increase the prediction accuracy by allowing stochastic formulations and bi-directional connections between neurons. In this paper, we investigate a newly developed deep learning model for time series prediction, namely Factored Conditional Restricted Boltzmann Machine (FCRBM), and extend it for electrical demand forecasting. The assessment is made on the EcoGrid dataset, originating from the Bornholm island experiment in Denmark, consisting of aggregated electric power consumption, local price and meteorological data collected from 1900 customers. The households are equipped with local generation and smart appliances capable of responding to realtime pricing signals. The results show that for the short-term (5 minute to 1 day ahead) prediction problems solved here, FCRBM outperforms the benchmark machine learning approach, i.e. Support Vector Machine.