Taifeng Pan^*

Journal of Quantum Computing, Vol.3, No.4, pp. 161-171, 2021, DOI:10.32604/jqc.2021.025373 - 10 January 2022

Abstract The rapid development of Internet of Things (IoT) technology has brought great convenience to people’s life. However, the security protection capability of IoT is weak and vulnerable. Therefore, more protection needs to be done for the security of IoT. The paper proposes an intrusion detection method for IoT based on multi GBDT feature reduction and hierarchical traffic detection model. Firstly, GBDT is used to filter the features of IoT traffic data sets BoT-IoT and UNSW-NB15 to reduce the traffic feature dimension. At the same time, in order to improve the reliability of feature filtering, this… More >

Pratik Roy^*, Saptarshi Sahoo, Amit Kumar Mandal, Indranil Basu

Journal of Quantum Computing, Vol.3, No.4, pp. 151-160, 2021, DOI:10.32604/jqc.2021.019864 - 10 January 2022

Abstract Quantum Key Distribution seems very promising as it offers unconditional security, that’s why it is being implemented by the tech giants of the networking industry and government. Having quantum phenomenon as a backbone, QKD protocols become indecipherable. Here we have focused on the complexities of quantum key distribution and how this technology has contributed to secure key communication. This article gives an updated overview of this technology and can serve as a guide to get familiar with the current trends of quantum cryptography. More >

Saasha Joshi^*, Deepti Gupta

Journal of Quantum Computing, Vol.3, No.4, pp. 137-150, 2021, DOI:10.32604/jqc.2021.018114 - 10 January 2022

Abstract This paper provides an introduction to a quantum search algorithm, known as Grover’s Algorithm, for unsorted search purposes. The algorithm is implemented in a search space of 4 qubits using the Python-based Qiskit SDK by IBM. While providing detailed proof, the computational complexity of the algorithm is generalized to n qubits. The implementation results obtained from the IBM QASM Simulator and IBMQ Santiago quantum backend are analyzed and compared. Finally, the paper discusses the challenges faced in implementation and real-life applications of the algorithm hitherto. Overall, the implementation and analysis depict the advantages of this More >

Xinlong Wu¹, Desheng Zheng^1,*, Kexin Zhang¹, Yanling Lai¹, Zhifeng Liu¹, Zhihong Zhang²

Journal of Quantum Computing, Vol.3, No.4, pp. 127-136, 2021, DOI:10.32604/jqc.2021.017251 - 10 January 2022

Abstract Image conversion refers to converting an image from one style to another and ensuring that the content of the image remains unchanged. Using Generative Adversarial Networks (GAN) for image conversion can achieve good results. However, if there are enough samples, any image in the target domain can be mapped to the same set of inputs. On this basis, the Cycle Consistency Generative Adversarial Network (CycleGAN) was developed. This article verifies and discusses the advantages and disadvantages of the CycleGAN model in image style conversion. CycleGAN uses two generator networks and two discriminator networks. The purpose… More >

Biying Deng¹ , Desheng Zheng^{1, *}, Zhifeng Liu¹ , Yanling Lai¹, Zhihong Zhang²

Journal of Quantum Computing, Vol.3, No.3, pp. 119-126, 2021, DOI:10.32604/jqc.2021.017250 - 21 December 2021

Abstract There are two difficult in the existing image restoration methods. One is that the method is difficult to repair the image with a large damaged, the other is the result of image completion is not good and the speed is slow. With the development and application of deep learning, the image repair algorithm based on generative adversarial networks can repair images by simulating the distribution of data. In the process of image completion, the first step is trained the generator to simulate data distribution and generate samples. Then a large number of falsified images More >

Linbei Wang¹ , Zaoyu Tao¹, Lina Wang^2,*, Yongjun Ren³

Journal of Quantum Computing, Vol.3, No.3, pp. 107-118, 2021, DOI:10.32604/jqc.2021.016857 - 21 December 2021

Abstract With the accelerating process of social informatization, our personal information security and Internet sites, etc., have been facing a series of threats and challenges. Recently, well-developed neural network has seen great advancement in natural language processing and computer vision, which is also adopted in intrusion detection. In this research, a hybrid model integrating MultiScale Convolutional Neural Network and Long Short-term Memory Network (MSCNN-LSTM) is designed to conduct the intrusion detection. Multi-Scale Convolutional Neural Network (MSCNN) is used to extract the spatial characteristics of data sets. And Long Short-term Memory Network (LSTM) is responsible for processing More >

Hongchao Ma^1,*, Yi Man¹, Xiao Xing², Zihan Zhuo², Mo Chen³

Journal of Quantum Computing, Vol.3, No.3, pp. 97-105, 2021, DOI:10.32604/jqc.2021.016775 - 21 December 2021

Abstract At present, the traditional blockchain for data storage and retrieval reflects the characteristics of slow data uploading speed, high cost, and transparency, and there are a lot of corresponding problems, such as not supporting private data storage, large data operation costs, and not supporting Data field query. This paper proposes a method of data encryption storage and retrieval based on the IOTA distributed ledger, combined with the fast transaction processing speed and zero-value transactions of the IOTA blockchain, through the Masked Authenticated Messaging technology, so that the data is encrypted in the data stream. The More >

Muhammad Haris^1,*, Muhammad Noman Hasan¹ , Abdul Basit², Shiyin Qin¹

Journal of Quantum Computing, Vol.3, No.3, pp. 89-95, 2021, DOI:10.32604/jqc.2021.016390 - 21 December 2021

Abstract This article presents a multilayer hybrid classical-quantum classifier for predicting the lifetime of LiFePO4 batteries using early degradation data. The multilayer approach uses multiple variational quantum circuits in cascade, which allows more parameters to be used as weights in a single run hence increasing accuracy and provides faster cost function convergence for the optimizer. The proposed classifier predicts with an accuracy of 92.8% using data of the first four cycles. The effectiveness of the hybrid classifier is also presented by validating the performance using untrained data with an accuracy of 84%. We also demonstrate More >

Chenjing Su¹, Xiaoyu Li^1,*, Mengru Li², Qinsheng Zhu², Hao Fu², Shan Yang³

Journal of Quantum Computing, Vol.3, No.2, pp. 79-87, 2021, DOI:10.32604/jqc.2021.016651 - 22 June 2021

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 More >

Lord Amoah^1,2,*, Ampofo Twumasi Kwabena³

Journal of Quantum Computing, Vol.3, No.2, pp. 65-77, 2021, DOI:10.32604/jqc.2021.020734 - 22 June 2021

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… More >