Qsco: A Quantum Scoring Module for Open-set Supervised Anomaly Detection

Open set anomaly detection (OSAD) is a crucial task that aims to identify abnormal patterns or behaviors in data sets, especially when the anomalies observed during training do not represent all possible classes of anomalies. The recent advances in quantum computing in handling complex data structures and improving machine learning models herald a paradigm shift in anomaly detection methodologies. This study proposes a Quantum Scoring Module (Qsco), embedding quantum variational circuits into neural networks to enhance the model's processing capabilities in handling uncertainty and unlabeled data. Extensive experiments conducted across eight real-world anomaly detection datasets demonstrate our model's superior performance in detecting anomalies across varied settings and reveal that integrating quantum simulators does not result in prohibitive time complexities. Our study validates the feasibility of quantum-enhanced anomaly detection methods in practical applications.

Distributed 3D-Beam Reforming for Hovering-Tolerant UAVs Communication over Coexistence: A Deep-Q Learning for Intelligent Space-Air-Ground Integrated Networks

In this paper, we present a novel distributed UAVs beam reforming approach to dynamically form and reform a space-selective beam path in addressing the coexistence with satellite and terrestrial communications. Despite the unique advantage to support wider coverage in UAV-enabled cellular communications, the challenges reside in the array responses' sensitivity to random rotational motion and the hovering nature of the UAVs. A model-free reinforcement learning (RL) based unified UAV beam selection and tracking approach is presented to effectively realize the dynamic distributed and collaborative beamforming. The combined impact of the UAVs' hovering and rotational motions is considered while addressing the impairment due to the interference from the orbiting satellites and neighboring networks. The main objectives of this work are two-fold: first, to acquire the channel awareness to uncover its impairments; second, to overcome the beam distortion to meet the quality of service (QoS) requirements. To overcome the impact of the interference and to maximize the beamforming gain, we define and apply a new optimal UAV selection algorithm based on the brute force criteria. Results demonstrate that the detrimental effects of the channel fading and the interference from the orbiting satellites and neighboring networks can be overcome using the proposed approach. Subsequently, an RL algorithm based on Deep Q-Network (DQN) is developed for real-time beam tracking. By augmenting the system with the impairments due to hovering and rotational motion, we show that the proposed DQN algorithm can reform the beam in real-time with negligible error. It is demonstrated that the proposed DQN algorithm attains an exceptional performance improvement. We show that it requires a few iterations only for fine-tuning its parameters without observing any plateaus irrespective of the hovering tolerance.