According to the rapid development of drone technologies, drones are widely used in many applications including military domains. In this paper, a novel situation-aware DRL- based autonomous nonlinear drone mobility control algorithm in cyber-physical loitering munition applications. On the battlefield, the design of DRL-based autonomous control algorithm is not straightforward because real-world data gathering is generally not available. Therefore, the approach in this paper is that cyber-physical virtual environment is constructed with Unity environment. Based on the virtual cyber-physical battlefield scenarios, a DRL-based automated nonlinear drone mobility control algorithm can be designed, evaluated, and visualized. Moreover, many obstacles exist which is harmful for linear trajectory control in real-world battlefield scenarios. Thus, our proposed autonomous nonlinear drone mobility control algorithm utilizes situation-aware components those are implemented with a Raycast function in Unity virtual scenarios. Based on the gathered situation-aware information, the drone can autonomously and nonlinearly adjust its trajectory during flight. Therefore, this approach is obviously beneficial for avoiding obstacles in obstacle-deployed battlefields. Our visualization-based performance evaluation shows that the proposed algorithm is superior from the other linear mobility control algorithms.

While witnessing the noisy intermediate-scale quantum (NISQ) era and beyond, 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. Towards enabling QFL under various channel conditions, in this article we develop a depth-controllable architecture of entangled slimmable quantum neural networks (eSQNNs), and propose an entangled slimmable QFL (eSQFL) that communicates the superposition-coded parameters of eS-QNNs. Compared to the existing depth-fixed QNNs, training the depth-controllable eSQNN architecture is more challenging due to high entanglement entropy and inter-depth interference, which are mitigated by introducing entanglement controlled universal (CU) gates and an inplace fidelity distillation (IPFD) regularizer penalizing inter-depth quantum state differences, respectively. Furthermore, we optimize the superposition coding power allocation by deriving and minimizing the convergence bound of eSQFL. In an image classification task, extensive simulations corroborate the effectiveness of eSQFL in terms of prediction accuracy, fidelity, and entropy compared to Vanilla QFL as well as under different channel conditions and various data distributions.

在自主驾驶环境中，同时保证实时和准确的对象检测至关重要。但是，现有的对象检测神经网络系统的特征是计算时间和准确性之间的权衡，因此必须优化这种权衡。幸运的是，在许多自动驾驶环境中，图像以连续的形式出现，提供了使用光流的机会。在本文中，我们利用光流估计来提高对象检测神经网络的性能。此外，我们提出了一个lyapunov优化框架，以实现稳定性的时间平均性能最大化。它可以自适应地确定是否使用光流程适合动态车辆环境，从而确保车辆的队列稳定性和同时的时间平均最高性能。为了验证关键思想，我们使用各种对象检测神经网络和光流估计网络进行数值实验。此外，我们通过Yolov3微小和Flownet2-S展示了可自配置的稳定检测，它们分别是实时对象检测网络和光流估计网络。在演示中，我们提出的框架将准确性提高了3.02％，检测到的对象数量增加了59.6％，并且用于计算功能的队列稳定性。

随着嘈杂的中间量子量子（NISQ）时代的开始，量子神经网络（QNN）最近已成为解决经典神经网络无法解决的问题的解决方案。此外，QCNN吸引了作为下一代QNN的注意力，因为它可以处理高维矢量输入。但是，由于量子计算的性质，经典QCNN很难提取足够数量的功能。在此激励的情况下，我们提出了一种新版本的QCNN，称为可伸缩量子卷积神经网络（SQCNN）。此外，使用QC的保真度，我们提出了一种名为ReverseDelity Trainity（RF-Train）的SQCNN培训算法，可最大程度地提高SQCNN的性能。

网络安全研究中的关键主题之一是自动COA（行动）攻击搜索方法。被动搜索攻击的传统COA攻击方法可能很困难，尤其是随着网络变大。为了解决这些问题，正在开发新的自动COA技术，其中，本文设计了一种智能的空间算法，以在可扩展网络中有效运行。除空间搜索外，还考虑了基于蒙特卡洛（MC）的时间方法来照顾时间变化的网络行为。因此，我们为可扩展和时变网络的时空攻击COA搜索算法提出了一个时空攻击。

尽管量子至高无上尚未到来，但最近在实用量子计算的迫在眉睫的时代，人们对​​确定量子机学习的潜力（QML）的兴趣越来越大。由此激励，在本文中，我们基于具有两个单独的可训练参数的单独维度的量子神经网络（QNN）的独特特征重新设计多代理增强学习（MARL）：影响输出Qubit状态和极点参数的角度参数：与输出测量基础相关。我们提出了将这种二元训练性作为元学习能力，我们提出了量子元marl（QM2ARL），该量子元MARL（QM2ARL）首先应用角度训练进行元学习，然后进行极点训练，以进行几次射击或局部QNN培训。为了避免过度拟合，我们在角度训练期间开发了一种将噪声注入到极域中的角度正则化技术。此外，通过将极点作为每个受过训练的QNN的内存地址利用，我们介绍了极点内存的概念，允许仅使用两参数极点值保存和加载经过训练的QNN。从理论上讲，我们证明了角度到极正则化下的角度训练的收敛性，并通过模拟证实了QM2ARL在获得高奖励和快速收敛方面的有效性，以及在快速适应时间变化环境中的极点记忆。

量子联合学习（QFL）最近受到了越来越多的关注，其中量子神经网络（QNN）集成到联邦学习（FL）中。与现有的静态QFL方法相反，我们在本文中提出了可靠的QFL（SLIMQFL），这是一个动态QFL框架，可以应对时变的通信通道和计算能量限制。通过利用QNN的独特性质，可以分别训练并动态利用其角度参数，从而使其可行。模拟结果证实了SLIMQFL比香草QFL更高的分类精度，尤其是在较差的通道条件下。

Federated learning (FL) is a key enabler for efficient communication and computing, leveraging devices' distributed computing capabilities. However, applying FL in practice is challenging due to the local devices' heterogeneous energy, wireless channel conditions, and non-independently and identically distributed (non-IID) data distributions. To cope with these issues, this paper proposes a novel learning framework by integrating FL and width-adjustable slimmable neural networks (SNN). Integrating FL with SNNs is challenging due to time-varying channel conditions and data distributions. In addition, existing multi-width SNN training algorithms are sensitive to the data distributions across devices, which makes SNN ill-suited for FL. Motivated by this, we propose a communication and energy-efficient SNN-based FL (named SlimFL) that jointly utilizes superposition coding (SC) for global model aggregation and superposition training (ST) for updating local models. By applying SC, SlimFL exchanges the superposition of multiple-width configurations decoded as many times as possible for a given communication throughput. Leveraging ST, SlimFL aligns the forward propagation of different width configurations while avoiding inter-width interference during backpropagation. We formally prove the convergence of SlimFL. The result reveals that SlimFL is not only communication-efficient but also deals with non-IID data distributions and poor channel conditions, which is also corroborated by data-intensive simulations.

在许多深层神经网络（DNN）应用中，在行业领域收集高质量数据的困难阻碍了DNN的实际使用。因此，转移学习的概念已经出现，该概念利用了在大规模数据集中训练的DNN的验证知识。因此，本文提出了受神经体系结构搜索（NAS）的启发的两阶段建筑微调。主要思想之一是突变，它使用给定的架构信息降低了搜索成本。此外，还考虑了早期停滞，这通过事先终止搜索过程来降低NAS成本。实验结果验证我们提出的方法可降低32.4％的计算和22.3％的搜索成本。

自动驾驶汽车和自主驾驶研究一直受到现代人工智能应用中主要有希望的前景。根据先进的驾驶员辅助系统（ADAS）的演变，自动驾驶车辆和自主驱动系统的设计变得复杂和安全至关重要。通常，智能系统同时和有效地激活ADAS功能。因此，必须考虑可靠的ADAS功能协调，安全地控制驱动系统。为了处理这个问题，本文提出了一种随机的对抗性模仿学习（RAIL）算法。铁路是一种新的无衍生仿制学习方法，用于具有各种ADAS功能协调的自主驾驶;因此，它模仿决策者的运作，可以使用各种ADAS功能控制自动驾驶。该方法能够培训涉及激光雷达数据的决策者，并控制多车道复合道环境中的自主驾驶。基于仿真的评估验证了所提出的方法实现了所需的性能。