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Identifying key nodes in networks is a fundamental problem in network science. This study proposes a quantum deep reinforcement learning (QDRL) framework that integrates reinforcement learning with a variational quantum graph neural network, effectively identifying distributed influential nodes while preserving the network’s fundamental topological properties. By leveraging principles of quantum computing, our method is designed to reduce model parameters and computational complexity compared to traditional neural networks. Trained on small networks, it demonstrated strong generalization across diverse scenarios. We compared the proposed algorithm with some classical node ranking and network dismantling algorithms on various synthetical and empirical networks. The results suggest that the proposed algorithm outperforms existing baseline methods. Moreover, in synthetic networks based on Erdős–Rényi and Watts–Strogatz models, QDRL demonstrated its capability to alleviate the issue of localization in network information propagation and node influence ranking. Our research provides insights into addressing fundamental problems in complex networks using quantum machine learning, demonstrating the potential of quantum approaches for network analysis tasks. Full article

While impressive progress has been already achieved in wide-bandgap (WBG) semiconductors such as 4H-SiC and GaN technologies, the lack of intelligent methodologies to control the gate drivers has prevented exploitation of the maximum potential of semiconductor chips from obtaining the desired device operations. Thus, a potent ongoing trend is to design a fast gate driver switching scheme to upgrade the performance of electronic equipment at the system level. To address this issue, this work proposed a novel intelligent scheme for the control of gate driver switching using the concept of quantum computation in machine learning. In particular, the quantum principle was incorporated into deep reinforcement learning (DRL) to address the hardware limitations of conventional computers and the growing amount of data sets. Taking potential benefit of the quantum theory, the DRL algorithm influenced by quantum specifications (referred to as QDRL) not only ameliorates the performance of the native algorithm on traditional computers but also enhances the progress of relevant research fields like quantum computing and machine learning. To test the practicability and usefulness of QDRL, a dc/dc parallel boost converter feeding constant power loads (CPLs) was chosen as the case study, and several power hardware-in-the-loop (PHiL) experiments and comparative analysis were performed. Full article

With the qualitative development of DC microgrids, the usage of different loads with unique conditions and features is now possible in electric power grids. Due to the negative impedance features of some loads, which are called constant power loads (CPLs), the control of DC power converters faces huge challenges from a stability point of view. Despite the significant advances in semiconductors, there is no upgrade in the control of gate drivers to exploit all potential of power electronic systems. In this paper, quantum computations are incorporated into artificial intelligence (AI) to stabilize a full-bridge (FB) DC-DC boost converter feeding CPL. Aiming to improve the bus voltage stabilization of the FB DC-DC boost converter, a quantum deep reinforcement learning (QDRL) control methodology is developed. By defining a reward function according to the specification of the FB power converter, the desired performance and control objectives are fulfilled. The main task of QDRL is to adjust the control coefficients embedded in the feedback controller to suppress the negative impedance effect resulting from deploying the CPLs. By deploying the potential advantages of quantum fundamentals, the deep reinforcement learning improved by quantum specifications will not only enhance the performance of the DRL algorithm on conventional processes but also advance related research areas such as quantum computing and AI. Unlike the basic quantum theory, which requires real quantum hardware, QDRL can be executed on classic computers. To examine the feasibility of the QDRL scheme, hardware-in-the-loop (HiL) examinations are conducted using the OPAL-RT. The comparison of the proposed controller with the classic state-of-the-art methodologies reveals the superiority and feasibility of QDRL-based control schemes in both the transient and steady-state conditions to other schemes. Analysis using various performance criteria, including the integral absolute error (IAE), integral time absolute error (ITAE), mean absolute error (MAE), and root mean square error (RMSE), demonstrates the dynamic improvement of the proposed scheme over sliding mode control (approximately 50%) and proportional integral control (approximately 100%). Full article