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.

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.

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.