Quantum computers have the potential to break the public-key cryptosystems widely used in key exchange and digital signature applications. To address this issue, quantum key distribution (QKD) offers a robust countermeasure against quantum computer attacks. Among various QKD schemes, BB84 is the most widely used and studied. However, BB84 implementations are inherently imperfect, resulting in quantum bit error rates (QBERs) even in the absence of eavesdroppers. Distinguishing between QBERs caused by eavesdropping and QBERs due to channel imperfections is fundamentally infeasible. In this context, this paper proposes and examines a practical method for detecting eavesdropping via partial intercept-and-resend attacks in the BB84 protocol. A key feature of the proposed method is its consideration of quantum system noise. The efficacy of this method is assessed by employing the Quantum Solver library in conjunction with backend simulators inspired by real quantum machines that model quantum system noise. The simulation outcomes demonstrate the method’s capacity to accurately estimate the eavesdropper’s interception density in the presence of system noise. Moreover, the results indicate that the estimation accuracy of the eavesdropper’s interception density in the presence of system noise is dependent on both the actual interception density value and the key length.

Long-distance quantum communication faces significant challenges due to photon loss and operational errors in fiber optic channels. Quantum repeaters (QRs) offer a promising solution by extending the communication range and enabling secure exchange of quantum information over continental distances. This article provides an overview of the three generations of QRs, comparing their technological requirements and challenges. We introduce the basic techniques, such as entanglement swapping, distillation, and error correction, and discuss their relevance to each generation of QRs. In addition, we present a future direction that integrates superconducting qubits with optical fibers, enabled by recent advances in microwave-optical transduction. This new hybrid architecture offers promising prospects for scalable and efficient quantum networks. It is also applicable in quantum key distribution protocols, such as E91.

One-sided heavy tailed distributions have been used in many engineering applications, ranging from teletraffic modelling to financial engineering. In practice, the most interesting heavy tailed distributions are those having a finite mean and a diverging variance. The LogNormal distribution is sometimes discarded from modelling heavy tailed phenomena because it has a finite variance, even when it seems the most appropriate one to fit the data. In this work we provide for the first time a LogNormal distribution having a finite mean and a variance which converges to a well-defined infinite value. This is possible thanks to the use of Non-Standard Analysis. In particular, we have been able to obtain a Non-Standard LogNormal distribution, for which it is possible to numerically and experimentally verify whether the expected mean and variance of a set of generated pseudo-random numbers agree with the theoretical ones. Moreover, such a check would be much more cumbersome (and sometimes even impossible) when considering heavy tailed distributions in the traditional framework of standard analysis.

This work presents and evaluates two threshold-based detection methods for the Six-State quantum key distribution protocol, considering a realistic scenario involving partial intercept–resend attack and channel noise. The statistical properties of the shared quantum bit error rate (QBER) are analyzed and used to estimate the attacker interception density from observed data. Building on this foundation, the work derives two optimal QBER detection thresholds designed to minimize both false positive and false negative rates, following, respectively, upper theoretical bounds and limit probability density function approach. A developed Qiskit simulation environment enables the evaluation and comparison of the two detection methods on simulated and real-inspired quantum systems with differing noise characteristics. This framework moves beyond theoretical analysis, allowing practical investigation of system noise effects on detection accuracy. Simulation results confirm that both methods are robust and effective, achieving high detection accuracy across all the tested configurations, thereby validating their applicability to real-world quantum communication systems.

In teletraffic engineering, M/G/1 queues are pivotal for optimizing network performance and operational efficiency. Closed formulas like the mean-value Pollaczek-Khinchin (MV-PK) are highly valued by practitioners due to their simplicity, facilitating rapid and flexible system analyses. This formula aids in verifying the reliability of complex simulations concerning M/G/1 models, especially when applicable. The challenge intensifies when dimensioning M/G/1 systems with heavy-tailed service time distributions. Simulation of such queues becomes notably arduous, necessitating increased number of samples and longer simulation durations. Moreover, verification becomes more intricate as the MV-PK formula cannot be straightforwardly applied in such cases. This paper extends the MV-PK formula to heavy tailed service distributions with infinite/infinitesimal moments using Nonstandard Analysis. Additionally, it shows how recently introduced Bounded Algorithmic Numbers (BANs) enables numerical verification of the extended PK formula via discrete-event simulations of the M/G/1 queue, even when predicted delay values are infinite. The implemented approach tends to converge fast and exhibits remarkable numerical robustness. It adheres to the principle of “write once, run multiple times", as the same source code for queue simulation handles both finite and infinite variance cases.

Maintaining optimal microclimatic conditions within greenhouses represents a significant challenge in modern agricultural contexts, where prediction systems play a crucial role in regulating temperature and humidity, thereby enabling timely interventions to prevent plant diseases or adverse growth conditions. In this work, we propose a novel approach which integrates a cascaded Feed-Forward Neural Network (FFNN) with the Granular Computing paradigm to achieve accurate microclimate forecasting and reduced computational complexity. The experimental results demonstrate that the accuracy of our approach is the same as that of the FFNN-based approach but the complexity is reduced, making this solution particularly well suited for deployment on edge devices with limited computational capabilities. Our innovative approach has been validated using a real-world dataset collected from four greenhouses and integrated into a distributed network architecture. This setup supports the execution of predictive models both on sensors deployed within the greenhouse and at the network edge, where more computationally intensive models can be utilized to enhance decision-making accuracy.