Space-air-ground integrated networks (SAGIN) provide seamless global coverage and cross-domain interconnection for the ubiquitous users in heterogeneous networks, which greatly promote the rapid development of intelligent mobile devices and applications. However, for mobile devices with limited computation capability and energy budgets, it is still a serious challenge to meet the stringent delay and energy requirements of computation-intensive ubiquitous mobile applications. Therefore, in view of the significant success in ground mobile networks, the introduction of mobile edge computing (MEC) in SAGIN has become a promising technology to solve the challenge. By deploying computing, cache, and communication resources in the edge of mobile networks, SAGIN MEC provides both low latency, high bandwidth, and wide coverage, substantially improving the quality of services for mobile applications. There are still many unprecedented challenges, due to its high dynamic, heterogeneous and complex time-varying topology. Therefore, efficient MEC deployment, resource management, and scheduling optimization in SAGIN are of great significance. However, most existing surveys only focus on either the network architecture and system model, or the analysis of specific technologies of computation offloading, without a complete description of the key MEC technologies for SAGIN. Motivated by this, this paper first presents a SAGIN network system architecture and service framework, followed by the descriptions of its characteristics and advantages. Then, the MEC deployment, network resources, edge intelligence, optimization objectives and key algorithms in SAGIN are discussed in detail. Finally, potential problems and challenges of MEC in SAGIN are discussed for future work.

In this paper, we describe the study of the faithful propagation of entangled orbital angular momentum states of light under atmospheric turbulence. The spatial mode is encoded in the Ince-Gauss modes that constitute a complete family of exact and orthogonal solutions of the paraxial wave equation in an elliptic coordinate system. Adaptive optics is employed to protect the entanglement from degradation, in which the threshold of turbulence strength could be enhanced for a reliable entanglement distribution. We find that the evolution of entanglements relies on ellipticity and shows the opposite trend when adopting adaptive optics. The turbulence strengths, at which the concurrences of various entangled states become zero, are different without adaptive optics but almost the same with adaptive optics. The trace of the density matrix is independent of the different ellipticity with or without adaptive optics. We believe that this investigation is useful for long-distance quantum communications and quantum networks using orbital angular momentum as information carriers.

We present an adaptive procedure for aligning quantum nonlocality experiments without any knowledge of the two-qudit state shared by the participating parties. The quantum state produced by the source, its unitary evolution, as well as the actual measurement bases remain unknown to both parties at all times. The entanglement of the quantum state helps establish desired correlations between individual measurement bases of the two distant parties. We implement the procedure in a fiber-based quantum key distribution (QKD) setup with polarization-entangled photons, where we do not rely on any additional alignment tools such as lasers or polarizers. In a QKD scenario, the procedure can be done without any additional measurements beyond those that are performed regardless.

Photons with orbital angular momentum provide an infinite-dimensional Hilbert space. Here, we study the propagation of such photons in atmospheric turbulence and obtain the Kraus operator representation for the decoherence-induced noise. We apply quantum error correction to suppress this decoherence. Specifically, we treat correction as an optimization problem in which the distance between the actual channel and the desired channel is minimized. This method provides significant channel fidelity enhancement irrespective of the turbulence strength.

Computing-in-memory (CIM) has become one of the most energy-efficient neural network accelerator architectures as it overcomes the problem of the memory wall. CIM with the novel non-volatile memory (nvCIM), such as resistive random access memory (RRAM), has potential performance gains. Along with the sparsity of deep neural networks (DNNs), nvCIM can further improve energy efficiency. In this work, we propose the weight and multiply-accumulation (MAC) sparsity-aware nvCIM system to optimize structured weight sparsity and dynamic MAC range. Experimental results show that the proposed sparsity-aware nvCIM system has a maximum 4.65× improvement in energy efficiency compared to the baseline with 89.56% accuracy of CIFAR-10 in the ResNet-18 network.

Quantum reinforcement learning (QRL) is a burgeoning area that lies at the convergence of quantum computing and machine learning. Based on the characteristics of quantum superposition and quantum entanglement, quantum reinforcement learning can achieve some quantum advantages on existing noisy intermediate-scale quantum devices compared to traditional reinforcement learning. Within the field of information systems, reinforcement learning techniques have been extensively explored for applications including intelligent decision support mechanisms, resource distribution strategies, and system enhancement under conditions of uncertainty. This paper analyzes the fundamental principles of quantum reinforcement learning and introduces research work and progress in related areas. This study examines the foundational concepts underlying quantum reinforcement learning and provides an overview of notable algorithmic strategies documented in contemporary research.