Chi-Chou Kao^1,*, Hung-Yi Lin²

Computer Systems Science and Engineering, Vol.48, No.6, pp. 1581-1594, 2024, DOI:10.32604/csse.2024.055073 - 22 November 2024

Abstract Layout synthesis in quantum computing is crucial due to the physical constraints of quantum devices where quantum bits (qubits) can only interact effectively with their nearest neighbors. This constraint severely impacts the design and efficiency of quantum algorithms, as arranging qubits optimally can significantly reduce circuit depth and improve computational performance. To tackle the layout synthesis challenge, we propose an algorithm based on integer linear programming (ILP). ILP is well-suited for this problem as it can formulate the optimization objective of minimizing circuit depth while adhering to the nearest neighbor interaction constraint. The algorithm aims… More >

Priyanka¹, Damodarakurup Sajeev², Shaik Ahmed³, Shankar Pidishety³, Ram Soorat^3,*

Journal of Quantum Computing, Vol.6, pp. 53-66, 2024, DOI:10.32604/jqc.2024.054000 - 14 November 2024

Abstract The present study delves into the application of investigating quantum state behaviour, particularly focusing on coherent and superposition states. These states, characterized by their remarkable stability and precision, have found extensive utility in various domains of quantum mechanics and quantum information processing. Coherent states are valuable for manipulating quantum systems with accuracy. Superposition states allow quantum systems to exist in numerous configurations at the same time, which paves the way for quantum computing’s capacity for parallel processing. The research accentuates the crucial role of quantum error correction (QEC) in ensuring the stability and reliability of… More >

Wenxia Wang, Jinchen Xu, Xiaodong Ding, Zhihui Song, Yizhen Huang, Xin Zhou, Zheng Shan^*

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1363-1390, 2024, DOI:10.32604/cmc.2024.054055 - 15 October 2024

Abstract With the wide application of drone technology, there is an increasing demand for the detection of radar return signals from drones. Existing detection methods mainly rely on time-frequency domain feature extraction and classical machine learning algorithms for image recognition. This method suffers from the problem of large dimensionality of image features, which leads to large input data size and noise affecting learning. Therefore, this paper proposes to extract signal time-domain statistical features for radar return signals from drones and reduce the feature dimension from 512 × 4 to 16 dimensions. However, the downscaled feature data… More >

Danyang Zheng, Jinchen Xv, Feng Yue, Qiming Du, Zhiheng Wang, Zheng Shan^*

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 1957-1974, 2024, DOI:10.32604/cmc.2024.048956 - 15 May 2024

Abstract As cloud quantum computing gains broader acceptance, a growing quantity of researchers are directing their focus towards this domain. Nevertheless, the rapid surge in demand for cloud-based quantum computing resources has led to a scarcity, which in turn hampers users from achieving optimal satisfaction. Therefore, cloud quantum computing service providers require a unified analysis and scheduling framework for their quantum resources and user jobs to meet the ever-growing usage demands. This paper introduces a new multi-programming scheduling framework for quantum computing in a cloud environment. The framework addresses the issue of limited quantum computing resources More >

Hosam Alhakami^*

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 329-356, 2024, DOI:10.32604/cmc.2023.043439 - 30 January 2024

Abstract The rapid growth of the Internet of Things (IoT) operations has necessitated the incorporation of quantum computing technologies to meet its expanding needs. This integration is motivated by the need to solve the specific issues provided by the expansion of IoT and the potential benefits that quantum computing can offer in this scenario. The combination of IoT and quantum computing creates new privacy and security problems. This study examines the critical need to prevent potential security concerns from quantum computing in IoT applications. We investigate the incorporation of quantum computing approaches within IoT security frameworks,… More >

Giuseppe Corrente^1,2,*, Carlo Vincenzo Stanzione^3,4, Vittoria Stanzione⁵

Journal of Quantum Computing, Vol.5, pp. 55-70, 2023, DOI:10.32604/jqc.2023.044786 - 14 December 2023

Abstract The Hamiltonian cycle problem (HCP), which is an NP-complete problem, consists of having a graph G with nodes and m edges and finding the path that connects each node exactly once. In this paper we compare some algorithms to solve a Hamiltonian cycle problem, using different models of computations and especially the probabilistic and quantum ones. Starting from the classical probabilistic approach of random walks, we take a step to the quantum direction by involving an ad hoc designed Quantum Turing Machine (QTM), which can be a useful conceptual project tool for quantum algorithms. Introducing several More >

Amit Saxena¹, Smita Saxena^2,*

Journal of Quantum Computing, Vol.5, pp. 1-13, 2023, DOI:10.32604/jqc.2023.044555 - 09 November 2023

Abstract Quantum computing is a promising new approach to tackle the complex real-world computational problems by harnessing the power of quantum mechanics principles. The inherent parallelism and exponential computational power of quantum systems hold the potential to outpace classical counterparts in solving complex optimization problems, which are pervasive in machine learning. Quantum Support Vector Machine (QSVM) is a quantum machine learning algorithm inspired by classical Support Vector Machine (SVM) that exploits quantum parallelism to efficiently classify data points in high-dimensional feature spaces. We provide a comprehensive overview of the underlying principles of QSVM, elucidating how different… More >

MD. Yasar Arafath^1,*, A. Niranjil Kumar²

Computer Systems Science and Engineering, Vol.46, No.2, pp. 2489-2508, 2023, DOI:10.32604/csse.2023.035732 - 09 February 2023

Abstract For intelligent surveillance videos, anomaly detection is extremely important. Deep learning algorithms have been popular for evaluating real-time surveillance recordings, like traffic accidents, and criminal or unlawful incidents such as suicide attempts. Nevertheless, Deep learning methods for classification, like convolutional neural networks, necessitate a lot of computing power. Quantum computing is a branch of technology that solves abnormal and complex problems using quantum mechanics. As a result, the focus of this research is on developing a hybrid quantum computing model which is based on deep learning. This research develops a Quantum Computing-based Convolutional Neural Network… More >

Abdullah M. Basahel, Mohammad Yamin^*

Computer Systems Science and Engineering, Vol.46, No.1, pp. 209-224, 2023, DOI:10.32604/csse.2023.034449 - 20 January 2023

Abstract Recent advancements in the Internet of Things (Io), 5G networks, and cloud computing (CC) have led to the development of Human-centric IoT (HIoT) applications that transform human physical monitoring based on machine monitoring. The HIoT systems find use in several applications such as smart cities, healthcare, transportation, etc. Besides, the HIoT system and explainable artificial intelligence (XAI) tools can be deployed in the healthcare sector for effective decision-making. The COVID-19 pandemic has become a global health issue that necessitates automated and effective diagnostic tools to detect the disease at the initial stage. This article presents… More >

Xi Huang^1,2, Shibin Zhang^1,2,*, Chen Lin^1,2, Jinyue Xia³

Computer Systems Science and Engineering, Vol.45, No.3, pp. 2783-2794, 2023, DOI:10.32604/csse.2023.032190 - 21 December 2022

Abstract In the objective world, how to deal with the complexity and uncertainty of big data efficiently and accurately has become the premise and key to machine learning. Fuzzy support vector machine (FSVM) not only deals with the classification problems for training samples with fuzzy information, but also assigns a fuzzy membership degree to each training sample, allowing different training samples to contribute differently in predicting an optimal hyperplane to separate two classes with maximum margin, reducing the effect of outliers and noise, Quantum computing has super parallel computing capabilities and holds the promise of faster… More >