Quantum networks connect devices to facilitate the transmission of quantum bits (qubits), harnessing quantum mechanics for groundbreaking telecommunications applications. The quantum state exchange rely on quantum entangled particles which have to be sent from a Quantum Base Station (QBS) to Quantum Nodes (QNs) through an optical channel. Free Space Optical (FSO) links promise flexibility and cost advantages over the classical fiber-based infrastructure. However, the absence of Line of Sight (LoS) between the QBS and QNs may completely disrupt the communication. Therefore, this work focuses on an Intelligent Reflective Surface (IRS)-assisted Unmanned Aerial Vehicle (UAV)-aided FSO quantum network. An optimization problem is formulated to fairly maximize qubit reception at QNs by optimizing UAV hovering locations, constrained by fidelity and link-per-IRS limits. The intractability of the original problem is first tackled with a sigmoid-based approximations, subsequently split into three sub-problems, sequentially solved applying the Successive Convex Approximation (SCA) technique. Numerical results (i) validate the approximation accuracy, and (ii) demonstrates the effectiveness of the proposal against three baseline approaches, showing an order of magnitude increase in terms of fairness.

Quantum Internet (QI) is a system of interconnected quantum computers able to exchange information encoded in the so called quantum bits (qubits). Differently from the classical counterpart, qubits benefit from a manifold properties guaranteed by quantum mechanics, such as superposition and entanglement. Despite the fact that quantum networks bring significant advantages, several phenomena can negatively impact the overall system, potentially hindering communication. In order to evaluate the network performance, a comprehensive probability expression is derived in this work to ultimately determine how many qubits are expected to be successfully received by nodes. On this basis, a Mixed-Integer Non-Linear Programming (MINLP) problem is formulated to fairly maximize the qubits exchanged between node pairs and jointly optimize (i) the position of the quantum source, and (ii) the entanglement distribution plan. To cope with the non-convexity of the problem, an iterative optimization algorithm, leveraging Block Coordinate Descendent (BCD) and Successive Convex Approximation (SCA) techniques, is proposed. A thorough simulation campaign is conducted to corroborate the theoretical findings. Numerical results demonstrates, under different parameter setups, that the proposed algorithm provides superior performance with respect to a baseline approach.

This contribution proposes a novel task offloading scheme for robotics applications in Information-Centric Networks. The reference scenario includes heterogeneous agents connected to various wireless Network Attachment Points, asking for computational services to Multi-access Edge Computing servers deployed at the network edge. It also uses Information-Centric Networking facilities to implement resource announcement, resource discovery, and service provisioning. The effectiveness of the conceived solution has been tested through experimental tests in realistic scenarios.