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.

  • New Quantum Private Comparison Using Hyperentangled GHZ State
  • Abstract In this paper, we propose a new protocol designed for quantum private comparison (QPC). This new protocol utilizes the hyperentanglement as the quantum resource and introduces a semi-honest third party (TP) to achieve the objective. This protocol’s quantum carrier is a hyperentangled three-photon GHZ state in 2 degrees of freedom (DOF), which could have 64 combinations. The TP can decide which combination to use based on the shared key information provided from a quantum key distribution (QKD) protocol. By doing so, the security of the protocol can be improved further. Decoy photon technology is also used as another means of… More
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  • Malware Detection Based on Multidimensional Time Distribution Features
  • Abstract Language detection models based on system calls suffer from certain false negatives and detection blind spots. Hence, the normal behavior sequences of some malware applications for a short period can become malicious behavior within a certain time window. To detect such behaviors, we extract a multidimensional time distribution feature matrix on the basis of statistical analysis. This matrix mainly includes multidimensional time distribution features, multidimensional word pair correlation features, and multidimensional word frequency distribution features. A multidimensional time distribution model based on neural networks is built to detect the overall abnormal behavior within a given time window. Experimental evaluation is… More
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  • A Bi-Histogram Shifting Contrast Enhancement for Color Images
  • Abstract Recent contrast enhancement (CE) methods, with a few exceptions, predominantly focus on enhancing gray-scale images. This paper proposes a bihistogram shifting contrast enhancement for color images based on the RGB (red, green, and blue) color model. The proposed method selects the two highest bins and two lowest bins from the image histogram, performs an equalized number of bidirectional histogram shifting repetitions on each RGB channel while embedding secret data into marked images. The proposed method simultaneously performs both right histogram shifting (RHS) and left histogram shifting (LHS) in each histogram shifting repetition to embed and split the highest bins while… More
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  • Improved Prediction and Understanding of Glass-Forming Ability Based on Random Forest Algorithm
  • Abstract As an ideal material, bulk metallic glass (MG) has a wide range of applications because of its unique properties such as structural, functional and biomedical materials. However, it is difficult to predict the glass-forming ability (GFA) even given the criteria in theory and this problem greatly limits the application of bulk MG in industrial field. In this work, the proposed model uses the random forest classification method which is one of machine learning methods to solve the GFA prediction for binary metallic alloys. Compared with the previous SVM algorithm models of all features combinations, this new model is successfully constructed… More
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