Oliver Crampton^1,*, Panagiotis Promponas^1,2, Richard Chen¹, Paul Polakos¹, Leandros Tassiulas², Louis Samuel¹

Journal of Quantum Computing, Vol.7, pp. 1-15, 2025, DOI:10.32604/jqc.2025.061275 - 21 March 2025

Abstract Distributed Quantum Computing (DQC) provides a means for scaling available quantum computation by interconnecting multiple quantum processor units (QPUs). A key challenge in this domain is efficiently allocating logical qubits from quantum circuits to the physical qubits within QPUs, a task known to be NP-hard. Traditional approaches, primarily focused on graph partitioning strategies, have sought to reduce the number of required Bell pairs for executing non-local CNOT operations, a form of gate teleportation. However, these methods have limitations in terms of efficiency and scalability. Addressing this, our work jointly considers gate and qubit teleportations introducing… More >

Ziao Han, Hui Li^*, Kai Lu, Shujuan Liu, Mingmei Ju

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5097-5113, 2025, DOI:10.32604/cmc.2025.059577 - 06 March 2025

Abstract Traditional quantum circuit scheduling approaches underutilize the inherent parallelism of quantum computation in the Noisy Intermediate-Scale Quantum (NISQ) era, overlook the inter-layer operations can be further parallelized. Based on this, two quantum circuit scheduling optimization approaches are designed and integrated into the quantum circuit compilation process. Firstly, we introduce the Layered Topology Scheduling Approach (LTSA), which employs a greedy algorithm and leverages the principles of topological sorting in graph theory. LTSA allocates quantum gates to a layered structure, maximizing the concurrent execution of quantum gate operations. Secondly, the Layerwise Conflict Resolution Approach (LCRA) is proposed.… More >

Sonia Khan¹, Naqash Younas², Musaed Alhussein³, Wahib Jamal Khan², Muhammad Shahid Anwar^4,*, Khursheed Aurangzeb³

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2641-2660, 2025, DOI:10.32604/cmes.2025.060973 - 03 March 2025

Abstract Effective resource management in the Internet of Things and fog computing is essential for efficient and scalable networks. However, existing methods often fail in dynamic and high-demand environments, leading to resource bottlenecks and increased energy consumption. This study aims to address these limitations by proposing the Quantum Inspired Adaptive Resource Management (QIARM) model, which introduces novel algorithms inspired by quantum principles for enhanced resource allocation. QIARM employs a quantum superposition-inspired technique for multi-state resource representation and an adaptive learning component to adjust resources in real time dynamically. In addition, an energy-aware scheduling module minimizes power More >

Dingjie Lu¹, Zhao Wang¹, Jun Liu¹, Yangfan Li¹, Wei-Bin Ewe¹, Liu Zhuangjian^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.31, No.2, pp. 1-1, 2024, DOI:10.32604/icces.2024.010961

Abstract This study heralds a new era in computational mechanics through the integration of Quantum Computing with the Finite Element Method (FEM), representing a quantum leap forward in addressing complex engineering simulations. Our approach utilizes Variational Quantum Algorithms (VQAs) to tackle challenges that have been traditionally well-solved on classical computers yet pose significant obstacles in the quantum computing domain. This innovation not only surmounts these challenges but also extends the applicability of quantum computing to real-world engineering problems, moving beyond mere conceptual demonstrations of quantum computing in numerical methods. The development of a novel strategy for… More >

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 >