Secure Multiparty Computation (SMC) enables multiple parties to cooperate securely without compromising their privacy. SMC has the potential to offer solutions for privacy obstacles in vehicular networks. However, classical SMC implementations suffer from efficiency and security challenges. To address this problem, two quantum communication technologies, Quantum Key Distribution (QKD) and Quantum Oblivious Key Distribution were utilised. These technologies supply symmetric and oblivious keys respectively, allowing fast and secure inter‐vehicular communications. These quantum technologies are integrated with the Faster Malicious Arithmetic Secure Computation with Oblivious Transfer (MASCOT) protocol to form a Quantum Secure Multiparty Computation (QSMC) platform. A lane change service is implemented in which vehicles broadcast private information about their intention to exit the highway. The proposed QSMC approach provides unconditional security even against quantum computer attacks. Moreover, the communication cost of the quantum approach for the lane change use case has decreased by 97% when compared to the classical implementation. However, the computation cost has increased by 42%. For open space scenarios, the reduction in communication cost is especially important, because it conserves bandwidth in the free‐space radio channel, outweighing the increase in computation cost.

A Quantum Secure Multiparty Computation (QSMC) solution for lane change service in vehicular networks that uses two quantum technologies, Quantum Key Distribution (QKD) and Quantum Oblivious Key Distribution (QOKD) is proposed. This quantum‐based approach is resistant to quantum computer attacks and requires less communication resources compared to classical methods.image

In the technologically changing world, the demand for ultra‐reliable, faster, low power, and secure communication has significantly risen in recent years. Researchers have shown immense interest in emerging quantum computing (QC) due to its potentials of solving the computing complexity in the robust and efficient manner. It is envisioned that QC can act as critical enablers and strong catalysts to considerably reduce the computing complexities and boost the future of sixth generation (6G) and beyond communication systems in terms of their security. In this study, the fundamentals of QC, the evolution of quantum communication that encompasses a wide spectrum of technologies and applications and quantum key distribution, which is one of the most promising applications of quantum security, have been presented. Furthermore, various parameters and important techniques are also investigated to optimise the performance of 6G communication in terms of their security, computing, and communication efficiency. Towards the end, potential challenges that QC and quantum communication may face in 6G have been highlighted along with future directions.

The paper presents how quantum technologies will enable future 6G and what are the future research directions in this area.image

This paper applies a quantum machine learning technique to predict mobile users' trajectories in mobile wireless networks by using an approach called quantum reservoir computing (QRC). Mobile users' trajectories prediction belongs to the task of temporal information processing, and it is a mobility management problem that is essential for self‐organising and autonomous 6G networks. Our aim is to accurately predict the future positions of mobile users in wireless networks using QRC. To do so, the authors use a real‐world time series dataset to model mobile users' trajectories. The QRC approach has two components: reservoir computing (RC) and quantum computing (QC). In RC, the training is more computational‐efficient than the training of simple recurrent neural networks since, in RC, only the weights of the output layer are trainable. The internal part of RC is what is called the reservoir. For the RC to perform well, the weights of the reservoir should be chosen carefully to create highly complex and non‐linear dynamics. The QC is used to create such dynamical reservoir that maps the input time series into higher dimensional computational space composed of dynamical states. After obtaining the high‐dimensional dynamical states, a simple linear regression is performed to train the output weights and, thus, the prediction of the mobile users' trajectories can be performed efficiently. In this study, we apply a QRC approach based on the Hamiltonian time evolution of a quantum system. The authors simulate the time evolution using IBM gate‐based quantum computers, and they show in the experimental results that the use of QRC to predict the mobile users' trajectories with only a few qubits is efficient and can outperform the classical approaches such as the long short‐term memory approach and the echo‐state networks approach.

In this study, the authorspropose to use quantum mechanics to solve the machine learning task of mobility prediction in mobile wireless networks. This task is nonlinear and belongs to real‐world temporal information processing tasks such as time‐dependent signal processing, stock‐market prediction, natural language processing etc. The combination of quantum mechanics and machine learning can help solving these real‐world temporal information processing tasks. The proposed machine learning approach belongs to the reservoir computing (RC) framework which is inspired by how the brain processes information.image

In quantum key distribution‐secured optical networks (QKD‐ONs), constrained network resources limit the success probability of QKD lightpath requests (QLRs). Thus, the selection of an appropriate route and the efficient utilisation of network resources for establishment of QLRs are the essential and challenging problems. This work addresses the routing and resource assignment (RRA) problem in the quantum signal channel of QKD‐ONs. The RRA problem of QKD‐ONs is a complex decision making problem, where appropriate solutions depend on understanding the networking environment. Motivated by the recent advances in deep reinforcement learning (DRL) for complex problems and also because of its capability to learn directly from experiences, DRL is exploited to solve the RRA problem and a DRL‐based RRA scheme is proposed. The proposed scheme learns the optimal policy to select an appropriate route and assigns suitable network resources for establishment of QLRs by using deep neural networks. The performance of the proposed scheme is compared with the deep‐Q network (DQN) method and two baseline schemes, namely, first‐fit (FF) and random‐fit (RF) for two different networks, namely The National Science Foundation Network (NSFNET) and UBN24. Simulation results indicate that the proposed scheme reduces blocking by 7.19%, 10.11%, and 33.50% for NSFNET and 2.47%, 3.20%, and 19.60% for UBN24 and improves resource utilisation up to 3.40%, 4.33%, and 7.18% for NSFNET and 1.34%, 1.96%, and 6.44% for UBN24 as compared with DQN, FF, and RF, respectively.

This work addresses the routing and resource assignment problem in QKD‐secured optical networks, explores the deep reinforcement learning (DRL) method, and proposes a DRL‐based routing and resource assignment scheme. Simulation results indicate that the proposed scheme outperforms the baseline schemes in terms of blocking probability and resource utilisation.image