Spurred by the limited availability of quantum resources, known as qubits, in recent quantum computers, quantum federated learning (QFL) is drawing attention. Due to its ability to fully utilize distributed qubits, QFL is suitable for developing quantum algorithms. QFL achieves local quantum gradients using the parameter-shift rule (P-S rule) and aggregates them, effectively coping with the limited number of qubits in each quantum computer. However, realizing QFL remains challenging due to the characteristics of the P-S rule, which requires two forward passes to compute the quantum gradients in each local quantum computer. These challenges become even more severe when the aggregation of the local quantum gradients occurs under heterogeneous channel conditions and data distributions. Motivated by this, this paper proposes the joint P-S rule, which eliminates the aggregation process in QFL and instead directly achieves the global quantum gradients. Furthermore, this paper proposes joint efficient quantum federated learning (Joint EQFL) that leverages successive interference cancellation and divergence-based clustering for achieving stability under heterogeneous channel conditions and robustness to heterogeneous data distributions. This paper analyzes the convergence and corroborates the superiority of Joint EQFL.

Achieving global space-air-ground integrated network (SAGIN) access only with CubeSats presents significant challenges such as the access sustainability limitations in specific regions (e.g.,polar regions) and the energy efficiency limitations in CubeSats. To tackle these problems, high-altitude long-endurance unmanned aerial vehicles (HALE-UAVs) can complement these CubeSat shortcomings for providing cooperatively global access sustainability and energy efficiency. However, as the number of CubeSats and HALE-UAVs, increases, the scheduling dimension of each ground station (GS) increases. As a result, each GS can fall into the curse of dimensionality, and this challenge becomes one major hurdle for efficient global access. Therefore, this paper provides a quantum multi-agent reinforcement Learning (QMARL)-based method for scheduling between GSs and CubeSats/HALE-UAVs in order to improve global access availability and energy efficiency. The main reason why the QMARL-based scheduler can be beneficial is that the algorithm facilitates a logarithmic-scale reduction in scheduling action dimensions, which is one critical feature as the number of CubeSats and HALE-UAVs expands. Additionally, individual GSs have different traffic demands depending on their locations and characteristics, thus it is essential to provide differentiated access services. The superiority of the proposed scheduler is validated through data-intensive experiments in realistic CubeSat/HALE-UAV settings.

Satellites have been envisioned as primary non-terrestrial networks capable of seamless global network and surveillance services. Among various satellite types, Cube Satellites (CubeSats) have been actively researched because multiple CubeSats can be conveniently positioned in a target orbit simultaneously and in proximity to Earth. However, CubeSats are small-scale, and thus, they are not able to accommodate a sizable battery, imposing constraints on the duration of their mission. Considering this energy limitation, in order to realize global network services using multiple CubeSats, this paper proposes a novel two-stage Reinforcement Learning (RL) algorithm for energy-efficient CubeSats where RL is utilized for dynamic control under uncertainty. Firstly, sustainable control for single-CubeSat orbital maneuver is considered using deep deterministic policy gradient for vertical position adjustment over a continuous action domain. Secondly, a novel quantum multi-agent RL algorithm for multi-CubeSat cooperative scheduling is designed to realize action dimension reduction into a logarithmic scale based on our proposed Projection-Valued Measure (PVM) over the quantum domain. It is highlighted that our considering two single- and multi-CubeSat problems cannot be separately considered for extreme energy management. The performance evaluation results demonstrate that the proposed algorithm outperforms other benchmarks with 1.51× higher performance in orbital control, 2.71× higher converged reward in enormous action dimensions, and 2.27× higher average network performance.

This paper introduces a novel resilient algorithm designed for distributed unmanned aerial vehicles (UAVs) in dynamic and unreliable network environments. Initially, the UAVs should be trained via multi-agent reinforcement learning (MARL) for autonomous mission-critical operations and are fundamentally grounded by centralized training and decentralized execution (CTDE) using a centralized MARL server. In this situation, it is crucial to consider the case where several UAVs cannot receive CTDE-based MARL learning parameters for resilient operations in unreliable network conditions. To tackle this issue, a communication graph is used where its edges are established when two UAVs/nodes are communicable. Then, the edge-connected UAVs can share their training data if one of the UAVs cannot be connected to the CTDE-based MARL server under unreliable network conditions. Additionally, the edge cost considers power efficiency. Based on this given communication graph, message-passing is used for electing the UAVs that can provide their MARL learning parameters to their edge-connected peers. Lastly, performance evaluations demonstrate the superiority of our proposed algorithm in terms of power efficiency and resilient UAV task management, outperforming existing benchmark algorithms.

This paper proposes a novel quantum style transfer (QST) in hybrid quantum-classical computing. QST leverages quantum computing's ability to process high-dimensional data efficiently. Our approach aims to decrease both inference time and complexity while maintaining performance, presenting a viable solution that enhances the scalability and efficiency of image generation technologies.

Audio-visual alignment using video data is a conventional approach for the self-supervision of multi-modal representation learning. Nevertheless, the presence of background music, external noise, and human conversational audio might lead to misalignment between the audio and visual elements in videos. In this paper, we introduce a method that concurrently identifies erroneous videos and trains the multi-modal representation model. Throughout the training process of the multi-modal representation model, we enhance a module responsible for detecting misaligned audio-visual videos, establishing accurate audiovisual pairs by eliminating erroneous videos. The misaligned audio-visual video detection module features an architecture based on VQ-VAE, extending to consider label information from input videos, effectively reconstructing video-based features along with labels. We evaluate our method on the tasks of video recognition and video retrieval on UCF-51 and UCF-101 datasets, achieving competitive performance with existing representation learning methods for audio-visual knowledge transfer.

This paper proposes SafeGPT, a two-tiered framework that integrates generative pretrained transformers (GPTs) with reinforcement learning (RL) for efficient and reliable un-manned aerial vehicle (UAV) last-mile deliveries. In the proposed design, a Global GPT module assigns high-level tasks such as sector allocation, while an On-Device GPT manages real-time local route planning. An RL-based safety filter monitors each GPT decision and overrides unsafe actions that could lead to battery depletion or duplicate visits, effectively mitigating hallucinations. Furthermore, a dual replay buffer mechanism helps both the GPT modules and the RL agent refine their strategies over time. Simulation results demonstrate that SafeGPT achieves higher delivery success rates compared to a GPT-only baseline, while substantially reducing battery consumption and travel distance. These findings validate the efficacy of combining GPT-based semantic reasoning with formal safety guarantees, contributing a viable solution for robust and energy-efficient UAV logistics.

Reinforcement learning (RL) using conventional neural networks (NN) has significantly progressed in various applications. However, conventional RL needs help training in environments with large-scale action dimensions, such as coordinated mobility/satellite systems. Quantum reinforcement learning (QRL) with quantum NN (QNN) can address this problem through superposition and entanglement, one of the great features of quantum mechanics. Based on its ‘i) fast convergence’ and ‘ii) high scalability’, unique advantages of QRL that distinguish it from conventional RL, this paper highlights the potential for QRL utilization in coordinated mobility and satellite systems.

This paper proposes a novel algorithm for distributed multi unmanned aerial vehicles (UAVs) cooperation in dynamic and unstable network environments by employing joint multi-agent reinforcement learning (MARL) and message-passing. To realize MARL, our proposed algorithm utilizes a centralized training with distributed execution (CTDE) framework. However, CTDE-based algorithms should be able to recognize the communications between UAVs and centralized server, which is not possible in every single time step. Therefore, after conducting centralized training for MARL, the distribution of the model for distributed execution should be re-designed. For this objective, a conflict graph-based approach is used, which enables graph-edge if two UAVs can talk to each other. Based on this conflict graph construction, message-passing is used to select UAVs for communication with the server. The non-selected UAVs can receive their models from conflict graph-connected UAVs.

The stability of aircraft remains vulnerable to sudden external disturbances and unpredictable vortices. The aircraft's attitude angles undergo rapid changes due to random turbulence. Consequently, to ensure safety, it is essential to control the aircraft's control surfaces, i.e., ailerons, elevators, and rudder angles, to maintain its static stability. Although classical closed-loop control methods have been widely adopted, their limited adaptability to changing dynamics calls for more robust solutions. Reinforcement learning (RL) offers adaptive capabilities but often demands a large number of training parameters and substantial computational resources, which may be impractical for real-time lightweight aircraft applications. To overcome these limitations, this paper introduces a quantum aircraft with the quantum actorcritic networks-based aircraft control (QACN-AC) algorithm. By utilizing quantum neural networks (QNN), QACN-AC significantly reduces the number of parameters required for training, thus mitigating computational overhead while preserving robust control performance. The QACN-AC's effectiveness is validated through realistic simulations leveraging Boeing's B777 specifications. The results highlight QACN-AC's superiority over conventional RL, evidenced by a$1.25 \times$higher control performance and a$760 \times$reduction in the number of required parameters.

Low Earth orbit (LEO) satellite networks have emerged as a promising solution, offering advantages such as lower propagation delay, broader coverage, and rapid deployment capabilities. However, the dynamic topology and frequent handovers inherent in LEO satellite systems, coupled with limited onboard computational resources, necessitate the development of efficient and lightweight routing algorithms. Therefore, this paper proposes quantum reinforcement learning-based satellite routing (QRL-SR) tailored for LEO satellite networks. The QRL-SR algorithm addresses three critical considerations: (i) adapting to the dynamic and time-varying environment of LEO satellite networks; (ii) incorporating LEO satellite geometry by transforming celestial coordinate data, specifically two-line element, into orbital coordinate systems for accurate LEO satellite positioning over time; and (iii) being designed to be lightweight by leveraging QRL to reduce the number of training parameters. The proposed QRL-SR efficiently trains routing policies with fewer parameters, aligning with LEO satellites’ small-size, weight, and power (SWaP) constraints. The primary purpose of the QRL-SR-based LEO satellites is to reduce free space path loss, delay time, and the number of hops needed for routing through the inter-satellite links. Finally, experimental results demonstrate that the QRL-SR achieves routing performance comparable to or outperforms conventional algorithms while significantly reducing computational resources.

Federated learning (FL) has emerged as a powerful paradigm for decentralized training, particularly in privacy-sensitive fields such as medical Internet of Things (IoT) services, where data security is important. However, FL faces challenges due to nonindependent and identically distributed (non-IID) data among clients, which can lead to suboptimal performance. In addition, there is a risk of data leakage during the aggregation process. To address these issues, we propose a novel approach using an auction mechanism to filter out unreliable clients, ensuring that only trustworthy participants are involved in the learning process. The selected clients are organized in a ring topology, eliminating the need for a central server and thereby reducing the risk of data breaches. Additionally, we leverage quantum neural networks (QNNs) to enhance security further, utilizing the quantum no-cloning theorem to prevent the duplication of quantum parameters. The results demonstrate that our approach can handle non-IID data distributions effectively and improve model performance, even with small and imbalanced datasets.

In order to build realistic digital-twin systems, this article proposes a novel two-stage algorithm for high-quality digital-twin services in cloud-assisted multitier networks. In our proposed algorithm, the first stage is quantum multiagent reinforcement learning (QMARL)-based scheduling for differentiated quality control of individual segments of digital-twin virtual objects in our cloud. As the number of segments selected by each edge increases, the edge’s action dimension expands exponentially, posing significant challenges to learning with conventional MARL. To solve this problem, the quantum-inspired MARL-based scheduler is considered in order to reduce the scheduling action dimensions into a logarithmic-scale. For the scheduling formulation, age-of-information (AoI) is also considered for low-latency high-quality digital-twin services. Additionally, the second stage is for the fast and seamless distribution of differentiated quality-controlled segments of virtual objects. For this objective, each user requests its desired segments and one of nearby edges is selected. Among various approaches, this second stage considers second price auction for truthful and distributed computation. Furthermore, low-complexity computation can be realized by avoiding integer-programming-based computation which is NP-hard. The proposed two-stage algorithm achieves performance levels that are 8.33 and 1.18 times higher in terms of reward value in high dimensions and revenue, respectively, compared to other benchmarks.

According to the advances in quantum computing and distributed learning, quantum federated learning (QFL) has recently become an emerging field of study. In QFL, each quantum computer or device locally trains its quantum neural network (QNN) with trainable gates, and communicates only these gate parameters over classical channels, without costly quantum communications. To successfully opeate QFL under various and dynamic channel conditions in Internet of Things (IoT) environments, this article develops a novel depth-controllable architecture of entangled slimmable QNNs (eSQNNs), and thus, proposes an entangled slimmable QFL (eSQFL) that communicates the superposition-coded parameters of eSQNNs. Even though the proposed eSQNN-based eSQFL is superior, training the depth-controllable eSQNN architecture is challenging due to high-entanglement entropy and interdepth interference. Therefore, the proposed method in this article mitigates the interference using entanglement controlled universal (CU) gates and an inplace fidelity distillation (IPFD) regularizer penalizing interdepth quantum state differences, respectively. Furthermore, the proposed method optimizes the superposition coding power allocation by deriving and minimizing the convergence bound of eSQFL. The novelty of this work is evaluated via extensive simulations in terms of prediction accuracy, fidelity, and entropy compared to Vanilla QFL as well as under different channel conditions and various data distributions.

Quantum federated learning (QFL) incorporates the principles of quantum neural networks (QNNs) and federated learning (FL), ensuring privacy by updating quantum parameters to the server. However, server aggregation in general QFL involves quantum parameters from all local models, leading to performance degradation due to the participation of outlier heterogeneous local models. Inspired by this, a novel QFL algorithm is proposed to enhance learning performance by excluding outliers before aggregation using Lyapunov optimization at QFL server. This Lyapunov optimization is used to take care of the tradeoff between accuracy and latency, where the accuracy is associated with the freshness of aggregation. Additionally, an entropy-and-fidelity aware algorithm is proposed, which relies on the degree control to select outliers for exclusion comprehensively. This algorithm addresses data and quantum aspects by evaluating the imbalance of data classes in each local model using entropy and quantifying the dissimilarity in quantum states between the target model and each local model through fidelity. Experimental results demonstrate that the proposed algorithm outperforms benchmarks, effectively excluding heterogeneous local models to improve performance while ensuring stability.

This paper proposes a computation-efficient quantum convolutional neural network (CE-QCNN) architecture designed for autonomous driving applications. A key contribution of this work lies in the formulation of a tri-value qubit encoding (TQE) scheme, which compactly embeds three-channel RGB image data into single-qubit states via a sequence of quantum rotations. This strategy enables significant qubit resource reduction while preserving the representational richness of multi-channel visual inputs. The encoded quantum states are subsequently processed through parameterized quantum circuits for convolutional feature extraction, forming the core of the proposed CE-QCNN framework. To further improve learning stability and early-stage performance, a knowledge distillation (KD) strategy is employed, transferring supervision from a pretrained classical CNN model to the quantum network. The proposed model is evaluated on the KITTI dataset, a standard benchmark for autonomous driving, where it demonstrates both competitive detection accuracy and reduced computational complexity. These results substantiate the scalability and practical applicability of CE-QCNNs for future quantum-enhanced perception systems in real-time autonomous driving scenarios.

Recent advances in deep learning have successfully replaced classical algorithms with machine learning models based on neural networks (NNs). This is particularly prevalent in proactive caching. As NNs grows more capable as their size in terms of storage and computation increases, NN-based proactive caching achieves performance improvement. Nonetheless, there remain challenges in implementing NN-based proactive caching in realistic environments with dynamic user movement. These are due to the fixed structure of NNs that should expand the input size to match the dimensions of the input with the dimensions of the NN’s input units. To address these challenges, this paper proposes a scalable proactive caching framework, named slimmable federated reinforcement learning (SlimFRL). By adopting slimmable neural networks (SNNs) in FRL, our SlimFRL easily adjusts the widths of the SNNs during training according to the number of users. Moreover, due to the scalability of SNNs, our SlimFRL can set the appropriate input dimension while not using imputation, leading to performance improvement. This paper also validates the performance and advantages of SlimFRL in terms of reward and additional cost functions. Additionally, this paper proposes several training algorithms for SlimFRL and corroborates their superiority with convergence analysis and various experiments.

The early prediction of depressive mood episodes is crucial for effective intervention in patients with Major Depressive Disorder (MDD) and Bipolar Disorder (BD). This study explores a predictive framework leveraging digital phenotypic data collected from smartphones and smartwatches, with a focus on circadian rhythm indicators such as Dim Light Melatonin Onset (DLMO). Using data from 164 participants within the Mood Disorder Cohort Research Consortium in Korea, time-series features related to sleep, heart rate, activity levels, and light exposure were processed to predict mood episodes seven days in advance. Deep learning models, including LSTM, GRU, and an LSTM-GRU hybrid, were applied to analyze this data, with the GRU model achieving the highest recall (0.767) and the LSTM model displaying superior robustness across metrics. SHAP value analysis of DLMO-related variables further underscored the association between circadian rhythm disruptions and depressive episodes, with delayed wake-up times relative to ideal schedules linked to increased depressive symptoms. Our findings demonstrate the feasibility of using digital phenotypes for early detection of mood episodes. These results highlight the potential of automated monitoring systems in clinical practice, which enable proactive intervention strategies through continuous, objective monitoring of patient conditions.

This article proposes a distributed angular routing algorithm in time-varying dynamic low Earth orbit (LEO) satellite constellation networks. For designing satellite routing algorithms, it is essential to consider 1) distributed operation due to the difficulty in global centralized computation and 2) angle-based computation under the consideration of orbit coordinate systems. Therefore, our proposed routing algorithm is based on distributed angular computation. Moreover, the proposed algorithm is designed by the Markov decision process (MDP) for discrete-time sequential decision making in time-varying LEO satellite networks. As a result, this article proposes an MDP-based distributed angular routing (MDAR) algorithm for seamless LEO routing. Based on the reward formulation in terms of angular differences in MDP formulation, our proposed distributed angular routing algorithm pursues orbit-geometrically straight-line data delivery from the source to its associated destination. Finally, our proposed routing algorithm is evaluated in the realistic environment with real-world satellite data, i.e., two line elements (TLEs), and the results confirm that our proposed algorithm outperforms the others in terms of routing success rate, reward convergence, and successful throughput.

In order to build realistic metaverse systems, enabling high synchronization between physical-space and virtual meta-space is essentially required. For this purpose, this paper proposes a novel system-wide coordination algorithm for high synchronization under characteristics (i.e., highly realistic meta-space construction under the constraints of physical-space). The proposed algorithm consists of the following three stages. The first stage is quantum multi-agent reinforcement learning (QMARL)-based scheduling for low-delay temporal-synchronization using differentiated age-of-information (AoI) during data gathering in physical-space by observers for meta-space construction. This is beneficial for scalability according to action dimension reduction in reinforcement learning computation. The second stage is for creating virtual contents under delay constraints in meta-space based on the gathered data. When rendering regions that have received more user attention, avatar-popularity is considered for spatio-synchronization. Thus, a stabilized control mechanism is designed for time-average reality quality maximization for each region. The last stage is for caching based on avatar-popularity and AoI which can be helpful in constructing low-delay realistic meta-space. Furthermore, the concept of AoI is divided into two separate sub-concepts of physical AoI and virtual AoI such that the AoI in virtual meta-space can be thoroughly implemented.

This paper proposes a novel multi-agent deep reinforcement learning (MADRL)-based positioning algorithm for multiple unmanned aerial vehicles (UAVs) collaboration in mobile access applications where the UAVs work as mobile base stations. The primary objective of the proposed algorithm is to establish reliable mobile access networks for vehicle-to-everything (V2X) communications. This paper jointly considers energy-efficient UAV operation and reliable wireless communication services for realizing robust mobile access services. For the energy-efficient UAV operation, the reward function formulation of our proposed MADRL algorithm contains the features for UAV energy consumption models in order to realize efficient operations. Furthermore, for the reliable wireless communication services, the quality of service (QoS) requirements of individual users are considered as a part of reward function. Furthermore, this paper considers 60, GHz millimeter-wave (mmWave) mobile access for utilizing the benefits of i) ultra-wide-bandwidth for multi-Gbps high-speed communications and ii) high-directional communications for spatial reuse that is obviously good for avoiding interference among densely deployed users. Lastly, the comprehensive and data-intensive performance evaluation of the proposed MADRL-based algorithm for multi-UAV positioning is conducted. The results of these evaluations demonstrate that the proposed algorithm outperforms other existing algorithms.

According to the challenges related to the limited availability of quantum bits (qubits) in the era of noisy intermediate-scale quantum (NISQ), the immediate replacement of all components in existing network architectures with quantum computing devices may not be practical. As a result, implementing a hybrid quantum-classical system is regarded as one of effective strategies. In hybrid quantum-classical systems, quantum computing devices can be used for computation-intensive applications, such as massive scheduling in dynamic environments. Furthermore, one of most popular network applications is advanced social media services such as metaverse. Accordingly, this paper proposes an advanced multi-metaverse dynamic streaming algorithm in hybrid quantum-classical systems. For this purpose, the proposed algorithm consists of three stages. For the first stage, three-dimensional (3D) point cloud data gathering should be conducted using spatially scheduled observing devices from physical-spaces for constructing virtual multiple meta-spaces in metaverse server. This is for massive scheduling over dynamic situations, i.e., quantum multi-agent reinforcement learning-based scheduling is utilized for scheduling dimension reduction into a logarithmic-scale. For the second stage, a temporal low-delay metaverse server’s processor scheduler is designed for region-popularity-aware multiple virtual meta-spaces rendering contents allocation via modified bin-packing with hard real-time constraints. Lastly, a novel dynamic dynamic streaming algorithm is proposed for high-quality, differentiated, and stabilized meta-spaces rendering contents delivery to individual users via Lyapunov optimization theory. Our performance evaluation results verify that the proposed spatio-temporal algorithm outperforms benchmarks in various aspects over hybrid quantum-classical systems.

This study presents a novel deep reinforcement learning (DRL)-based handover (HO) protocol, called DHO, specifically designed to address the persistent challenge of long propagation delays in low-Earth orbit (LEO) satellite networks’ HO procedures. DHO skips the Measurement Report (MR) in the HO procedure by leveraging its predictive capabilities after being trained with a pre-determined LEO satellite orbital pattern. This simplification eliminates the propagation delay incurred during the MR phase, while still providing effective HO decisions. The proposed DHO outperforms the legacy HO protocol across diverse network conditions in terms of access delay, collision rate, and handover success rate, demonstrating the practical applicability of DHO in real-world networks. Furthermore, the study examines the trade-off between access delay and collision rate and also evaluates the training performance and convergence of DHO using various DRL algorithms.

In recent years, quantum neural network (QNN) based on quantum computing has attracted attention due to its potential for computation-acceleration and parallelism. However, the intrinsic limitations of QNN, where the output (i.e., observables) can only be obtained through a measurement process, pose scalability challenges. Motivated by this, this paper aims to address the scalability challenges by incorporating Pauli-Z measurement and Basis measurement. In conventional frameworks, QNN typically relies on classical fully connected networks (FCNs) or increases the number of qubits to achieve large output dimensions. However, by leveraging our proposed framework, this paper successfully expands the output dimensions to an exponential scale, surpassing the limitations imposed by the limited number of qubits without relying on FCNs. Through extensive experiments, this paper demonstrates that the proposed framework outperforms existing QNN frameworks in multi-class classification tasks that require numerous output dimensions.

Recently, research on quantum neural network (QNN) architectures has been attracted in various fields. Among them, the distributed computation of QNN has been actively discussed for privacy-preserving information management due to data and model distribution over multiple computing devices. Based on this concept, this paper proposes quantum split learning (QSL) which splits a single QNN architecture across multiple distributed computing devices to avoid entire QNN architecture exposure. In order to realize QSL design, this paper also proposes cross-channel pooling, which utilizes quantum state tomography. Our evaluation results verifies that QSL preserves privacy in classification tasks and also improves accuracy at most by 6.83% compared to existing methods.

It has been considered that urban air mobility (UAM), also known as drone-taxi or electrical vertical takeoff and landing (eVTOL), will play a key role in future transportation. By putting UAM into practical future transportation, several benefits can be realized, i.e., (i) the total travel time of passengers can be reduced compared to traditional transportation and (ii) there is no environmental pollution and no special labor costs to operate the system because electric batteries will be used in UAM system. However, there are various dynamic and uncertain factors in the flight environment, i.e., passenger sudden service requests, battery discharge, and collision among UAMs. Therefore, this paper proposes a novel cooperative multiagent deep reinforcement learning (MADRL) algorithm based on centralized training and distributed execution (CTDE) concepts for reliable and efficient passenger delivery in UAM networks. According to the performance evaluation results, we confirm that the proposed algorithm outperforms other existing algorithms in terms of the number of serviced passengers increase (30%) and the waiting time per serviced passenger decrease (26% ).

This paper proposes an efficient quantum train engine (EQuaTE), a novel tool for quantum machine learning software which plots gradient variances to check whether our quantum neural network (QNN) falls into local minima (called barren plateaus in QNN). EQuaTE can be realized via dynamic analysis of the undetermined probabilistic qubit states. Furthermore, the proposed EQuaTE is capable of HCI-based visual feedback such that software engineers can recognize barren plateaus via visualization, allowing the modification of QNN based on this information.

This paper proposes a novel centralized training and distributed execution (CTDE)-based multi-agent deep reinforcement learning (MADRL) method for multiple unmanned aerial vehicles (UAVs) control in autonomous mobile access applications. For the purpose, a single neural network is utilized in centralized training for cooperation among multiple agents while maximizing the total quality of service (QoS) in mobile access applications.

This article proposes a novel algorithm, named quantum multiagent actor–critic networks (QMACN) for autonomously constructing a robust mobile access system employing multiple unmanned aerial vehicles (UAVs). In the context of facilitating collaboration among multiple UAVs, the application of multiagent reinforcement learning (MARL) techniques is regarded as a promising approach. These methods enable UAVs to learn collectively, optimizing their actions within a shared environment, ultimately leading to more efficient cooperative behavior. Furthermore, the principles of quantum computing (QC) are employed in our study to enhance the training process and inference capabilities of the UAVs involved. By leveraging the unique computational advantages of QC, our approach aims to boost the overall effectiveness of the UAV system. However, employing a QC introduces scalability challenges due to the near intermediate-scale quantum (NISQ) limitation associated with qubit usage. The proposed algorithm addresses this issue by implementing a quantum centralized critic, effectively mitigating the constraints imposed by NISQ limitations. Additionally, the advantages of the QMACN with performance improvements in terms of training speed and wireless service quality are verified via various data-intensive evaluations. Furthermore, this article validates that a noise injection scheme can be used for handling environmental uncertainties in order to realize robust mobile access.

Guaranteeing real-time and accurate object detection simultaneously is paramount in autonomous driving environments. However, the existing object detection neural network systems are characterized by a tradeoff between computation time and accuracy, making it essential to optimize such a tradeoff. Fortunately, in many autonomous driving environments, images come in a continuous form, providing an opportunity to use optical flow. In this paper, we improve the performance of an object detection neural network utilizing optical flow estimation. In addition, we propose a Lyapunov optimization framework for time-average performance maximization subject to stability. It adaptively determines whether to use optical flow to suit the dynamic vehicle environment, thereby ensuring the vehicle’s queue stability and the time-average maximum performance simultaneously. To verify the key ideas, we conduct numerical experiments with various object detection neural networks and optical flow estimation networks. In addition, we demonstrate the self-configurable stabilized detection with YOLOv3-tiny and FlowNet2-S, which are the real-time object detection network and an optical flow estimation network, respectively. In the demonstration, our proposed framework improves the accuracy by 3.02%, the number of detected objects by 59.6%, and the queue stability for computing capabilities.

Augmented reality (AR) is one of emerging applications in modern multimedia systems research. Due to intensive time-consuming computations for AR visualization in mobile devices, quality-aware real-time computing under delay constraints is essentially required. Inspired by Lyapunov optimization framework, this paper proposes a time-average quality maximization method for the AR visualization under delay considerations.

We consider a scenario that utilizes road side units (RSUs) as distributed caches in connected vehicular networks. The goal of the use of caches in our scenario is for rapidly providing contents to connected vehicles under various traffic conditions. During this operation, due to the rapidly changed road environment and user mobility, the concept of age-of-information (AoI) is considered for (1) updating the cached information as well as (2) maintaining the freshness of cached information. The frequent updates of cached information maintain the freshness of the information at the expense of network resources. Here, the frequent updates increase the number of data transmissions between RSUs and MBS; and thus, it increases system costs, consequently. Therefore, the tradeoff exists between the AoI of cached information and the system costs. Based on this observation, the proposed algorithm in this paper aims at the system cost reduction which is fundamentally required for content delivery while minimizing the content AoI, based on Markov Decision Process (MDP) and Lyapunov optimization.

Recently, emerging media services over edge-assisted vehicular-to-infrastructure (V2I) networks are widely studied. A joint design of scheduling and streaming algorithms is essential for seamless and high-quality media services. In scheduling, the algorithm is designed under the consideration of heterogeneous computing at edges. In addition, the streaming algorithm is also proposed for maximizing the time-average streaming quality maximization subject to stability. It is verified that our proposed joint scheduling and streaming algorithm achieves desired performance improvements.

CCTV-based surveillance using unmanned aerial vehicles (UAVs) is considered a key technology for security in smart city environments.This article creates a case where the UAVs with CCTV-cameras fly over the city area for flexible and reliable surveillance services. UAVs should be deployed to cover a large area while minimizing overlapping and shadow areas for a reliable surveillance system. However, the operation of UAVs is subject to high uncertainty, necessitating autonomous recovery systems. This article develops a multiagent deep reinforcement learning-based management scheme for reliable industry surveillance in smart city applications. The core idea this article employs is autonomously replenishing the UAV's deficient network requirements with communications. Via intensive simulations, our proposed algorithm outperforms the state-of-the-art algorithms in terms of surveillance coverage, user support capability, and computational costs.

This article proposes a novel multiagent deep reinforcement learning-based algorithm which can realize federated learning (FL) computation with Internet-of-Underwater-Things (IoUT) devices in the ocean environment. According to the fact that underwater networks are relatively not easy to set up reliable links by huge fading compared to wireless free-space air medium, gathering all training data for conducting centralized deep learning training is not easy. Therefore, FL-based distributed deep learning can be a suitable solution for this application. In this IoUT network (IoUT-Net) scenario, the FL system needs to construct a global learning model by aggregating the local model parameters that are obtained from individual IoUT devices. In order to reliably deliver the parameters from IoUT devices to a centralized FL machine, base station like devices are needed. Therefore, a joint cell association and resource allocation (JCARA) method is required and it is designed inspired by multiagent deep deterministic policy gradient (MADDPG) to deal with distributed situations and unexpected time-varying states. The performance evaluation results show that our proposed MADDPG-based algorithm achieves 80% and 41% performance improvements than the standard actor–critic and DDPG, respectively, in terms of the downlink throughput.

The vehicles take a role of mobile devices so that resource management issue among them in cellular networks is getting highlighted. In addition, the millimeter-wave (mmWave) base station is expected to be included in existing heterogeneous networks. In this regard, we present a multi-agent deep reinforcement learning (MADRL) based resource allocation strategy for mobile devices in heterogeneous vehicular networks, which suffer from shortage issue of shared frequency band. The downlink throughput of each mobile device is cooperatively enhanced by the proposed MADRL method, and thus each mobile device associates with a base station and uses a frequency band within low delay.