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Journal of Quantum Computing

About the Journal

Journal of Quantum Computing is a high-impact, international journal publishing cutting-edge experimental and theoretical research in all areas of Quantum Computing and Information Science. Topics of interest include quantum computer science, Quantum machine learning, quantum secure communications, quantum information processing, quantum imaging and networking, quantum cryptography, entanglement and discord, quantum algorithms, quantum error correction and fault tolerance, and experimental platforms for quantum information.

  • Weighted Particle Swarm Clustering Algorithm for Self-Organizing Maps
  • Abstract The traditional K-means clustering algorithm is difficult to determine the cluster number, which is sensitive to the initialization of the clustering center and easy to fall into local optimum. This paper proposes a clustering algorithm based on self-organizing mapping network and weight particle swarm optimization SOM&WPSO (Self-Organization Map and Weight Particle Swarm Optimization). Firstly, the algorithm takes the competitive learning mechanism of a self-organizing mapping network to divide the data samples into coarse clusters and obtain the clustering center. Then, the obtained clustering center is used as the initialization parameter of the weight particle swarm optimization algorithm. The particle position… More
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  • A Novel Intrusion Detection Algorithm Based on Long Short Term Memory Network
  • Abstract In recent years, machine learning technology has been widely used for timely network attack detection and classification. However, due to the large number of network traffic and the complex and variable nature of malicious attacks, many challenges have arisen in the field of network intrusion detection. Aiming at the problem that massive and high-dimensional data in cloud computing networks will have a negative impact on anomaly detection, this paper proposes a Bi-LSTM method based on attention mechanism, which learns by transmitting IDS data to multiple hidden layers. Abstract information and high-dimensional feature representation in network data messages are used to… More
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  • A Survey of Time Series Data Visualization Methods
  • Abstract In the era of big data, the general public is more likely to access big data, but they wouldn’t like to analyze the data. Therefore, the traditional data visualization with certain professionalism is not easy to be accepted by the general public living in the fast pace. Under this background, a new general visualization method for dynamic time series data emerges as the times require. Time series data visualization organizes abstract and hard-to-understand data into a form that is easily understood by the public. This method integrates data visualization into short videos, which is more in line with the way… More
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  • A Position Self-Adaptive Method to Detect Fake Access Points
  • Abstract In recent years, with the maturity and popularity of Wi-Fi technology, wireless hotspots have been deployed on a large scale in public places. But at the same time, it brings many security issues that cannot be ignored. Among them, the fake access point attack is a very serious threat in wireless local area network. In this paper, we propose a method to detect fake access points in wireless local area network. First, our detection method is passive, which means there is almost no additional traffic will be generated during the program’s operation. Second, different from many existing methods, our method… More
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