This study realizes secure near-field location division multiple access through employing both a variational quantumcircuit and a nature-inspired algorithm. With the escalating demands of the sixth generation (6G) and beyond, there is growing interest in using large numbers of antenna elements,making near-field communications more practical Seizing this opportunity, we leverage near-field communications for a distinct multiple access technique, termed location division multipleaccess (LDMA), which capitalizes on spatial orthogonality to distinguish users by both angle and distance. Nevertheless, relying on beamforming to direct signals to distinct users poses a clear physical-layer security risk: adversaries might eavesdrop on messages intended for legitimate users, prompting the need tooptimize beamforming to enhance security while adhering towireless systems’ constraints. To make matters worse, conventional approaches typically entail multiple matrix inversions, and the optimization problem is far from trivial to solve due toits non-convexity and NP-hardness. To this end, our solutionleverages variational quantum circuits (VQC), motivated bythe potential benefits offered by quantum computing. On topof that, we improve upon the existing VQC workflows by integrating a classical algorithm, particularly, the differential evolution algorithm, thereby reducing quantum computational resource demands while maintaining high exploration efficiency. We further investigate how different quantum circuit depths influence the balance between expressibility and convergence. Simulation results reveal that our proposed scheme consistently outperforms conventional benchmarks in terms of secrecyrates, while simultaneously satisfying quality-of-service (QoS)constraints and power allocation requirements.

This paper propose a two-stage quantum neural network (QNN) framework for cell-free multiple-input and multiple-output (MIMO) wireless communication systems. Cell-free MIMO, which has been regarded as a key technology for enhancing the performance of the next-generation wireless communication systems, leverages the collective capability of multiple distributed access points (APs), allowing collaboration between them. However, optimizing cell-free MIMO can pose challenges for centralized optimization schemes. In particular, complexities associated with the joint optimizations of user-transmission assignment and transmission precoding, two factors which are of much importance for determining the quality-of-service, grow with the number of APs and served users. To this end, a unified scheme employing distributed QNNs is used to optimize downlink transmitter-user assignment and transmit precoding with the goal of maximizing the achieved sum rate. Firstly, the cloud processing unit, which holds holistic information about the particular wireless communication network, employs QNN to assign each AP to its designated mobile terminal. Secondly, the edge processing units, which are computed in proximity relative to the AP in order to reduce latency, estimate transmission precoding for their corresponding APs. Moreover, numerical results are presented to showcase the performance of the proposed protocol.

This study proposes a layerwise quantum-based deep reinforcement learning (LQ-DRL) method for optimizing continuous large space and time series problems using deep-layer training. The actions in LQ-DRL are optimized using a layerwise quantum embedding that leverages the advantages of quantum computing to maximize reward and reduce training loss. Moreover, this study employs a local loss to minimize the occurrence of barren plateaus phenomena and further enhance performance. As a particular case, the proposed scheme is employed to jointly optimize: 1) unmanned aerial vehicle (UAV) trajectory planning; 2) user grouping; and 3) power allocation for higher energy efficiency of a UAV as the reward. The combination of these optimized factors is referred to as action space in the presented LQ-DRL. The LQ-DRL is employed to solve the optimization problem due to its nonconvexity, continuous and large action space, and time-series domain. In a practical view, LQ-DRL aims to solve the issue of energy consumption related to limited-battery energy of a UAV base station (BS) while maintaining Quality of Service (QoS) for users, by gaining maximum energy efficiency as the reward. One of real applications, as an example, LQ-DRL can be employed to maximize the energy efficiency of a UAV BS in UAV empowered disaster recovery networks scenario. The quantum circuits of layerwise quantum embedding are presented to show the practical implementation in noisy intermediate-scale quantum computers. Based on the results, LQ-DRL outperformed the classical DRL by achieving higher effective dimension, rewards, and lower learning losses. In addition, better performances were achieved using more layers.

Quantum neural network with parallel training (called PS-QNN) is presented in this study to optimize wireless resource allocation. Instead of sending the whole dataset, each edge only requires to send the statistical parameters of the dataset; hence reducing the dimension of training data. As a particular case, the proposed PS-QNN is utilized to optimize transmit precoding and power allocation in non-orthogonal multiple access with multiple-input and multiple-output antennas (MIMO-NOMA). Compared to the conventional training method, analysis shows that the proposed parallel training yields a lower complexity, while achieving a comparable sum rate compared to conventional method.

This study proposes the utilization of quantum machine learning (QML) to maximize the energy efficiency of reconfigurable intelligent surface (RIS) assisted communication with rate-splitting multiple access (RSMA). The next-generation wireless communications are expected to yield significantly higher energy efficiency compared to that of the previous generations. In a multiuser system, energy efficiency can be defined as a benefit-to-cost ratio between the achievable sum-rate and the energy consumption, where enhancements in the former come at the expense of increases in the latter. Recently, the integration between RSMA and RISs has been advocated as a powerful mean to control this tradeoff. Indeed, RSMA can enhance the rate region while RISs can lead to reduced energy consumption thanks to the use of low-energy phase shifters. However, optimizing a RIS-aided RSMA communication system is faced with a computational burden given that the RIS enlarges the volume of the required channel information, which expands the information that needs to be processed by the optimization module, even when the optimization is based on conventional learning techniques. The proposed QML optimization framework, which orchestrates non-linear quantum unitary operations to compose the learning models, enjoys information processing gains thanks to state vector operations in multi-dimensional Hilbert space. It is composed of two trainable quantum-based learning models employed in an alternating manner: the first establishes the transmission precoding, and the second designs the RIS phase shifting. Numerical results show that the proposed QML delivers comparable performance to that of conventional optimization but with reduced complexity.

This work employs modular quantum machine learning (QML) to estimate the wireless channels in simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) aided communication systems. Although RISs, composed of low-energy phase-shifting elements, can enable controlled signal reflections to cover communication devices obstructed by blockages, the devices located behind the reflection-only surfaces cannot be covered as these structures now become blockages themselves. STAR-RISs solve this issue by allowing the transmission signals to be conveyed to the devices located behind the STAR-RIS structures. However, acquiring accurate channel information of devices in the reflection and transmission regions of a STAR-RIS is not a trivial task. To address this issue, this paper proposes a novel modular QML scheme that employs different quantum-based learning modules to (i) eliminate the noise from the coarse channel information, and (ii) estimate the channels of the devices in the reflection and transmission regions.

In this paper, the potential benefit of employing quantum neural networks (QNNs) for cell-free MIMO is explored. In particular, QNN-based scheme are used to optimize transmitter-user assignment in cell-free MIMO. The objective of the optimization is to maximize the minimum achieved sum rate. Although QNN has received increasing research attention owing to the potential benefit of quantum computation, its utilization for multi-transmitter scenario is still limited. As such, in this paper, we consider the QNN-based algorithm for optimal resourcea allocation in cell-free MIMO systems. We also demonstrate the advantage of our proposed QNN-based algorithm through the numerical results.

This study exploits a quantum neural network (QNN) for resource allocation in wireless communications. A QNN is presented to reduce time complexity while still maintaining performance. Moreover, a reinforcement-learning- inspired QNN (RL-QNN) is presented to improve the perfor- mance. Quantum circuit design of the QNN is presented to ensure the practical implementation in noisy intermediate-scale quantum (NISQ) computers. For the QNN, the complexity and the number of required qubits are analyzed as well. As a particular use case, the QNN is utilized for user grouping in non-orthogonal multiple access. The results reveal that the QNN schemes have lower complexities and similar performance in terms of the achievable sum rate when compared with that of the classical neural network.