Sunawar Khan¹, Tehseen Mazhar^1,2,*, Naila Sammar Naz¹, Fahed Ahmed¹, Tariq Shahzad³, Atif Ali⁴, Muhammad Adnan Khan^5,*, Habib Hamam^6,7,8,9

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2347-2401, 2025, DOI:10.32604/cmc.2025.066932 - 23 September 2025

Abstract Feature selection (FS) plays a crucial role in medical imaging by reducing dimensionality, improving computational efficiency, and enhancing diagnostic accuracy. Traditional FS techniques, including filter, wrapper, and embedded methods, have been widely used but often struggle with high-dimensional and heterogeneous medical imaging data. Deep learning-based FS methods, particularly Convolutional Neural Networks (CNNs) and autoencoders, have demonstrated superior performance but lack interpretability. Hybrid approaches that combine classical and deep learning techniques have emerged as a promising solution, offering improved accuracy and explainability. Furthermore, integrating multi-modal imaging data (e.g., Magnetic Resonance Imaging (MRI), Computed Tomography (CT), Positron… More >

James Hancock^*, Matthew Craven, Craig McNeile, Davide Vadacchino

Journal of Quantum Computing, Vol.7, pp. 55-79, 2025, DOI:10.32604/jqc.2025.068127 - 11 August 2025

Abstract This paper investigates Windfarm Layout Optimization (WFLO), where we formulate turbine placement considering wake effects as a Quadratic Unconstrained Binary Optimization (QUBO) problem. Wind energy plays a critical role in the transition toward sustainable power systems, but the optimal placement of turbines remains a challenging combinatorial problem due to complex wake interactions. With recent advances in quantum computing, there is growing interest in exploring whether hybrid quantum-classical methods can provide advantages for such computationally intensive tasks. We investigate solving the resulting QUBO problem using the Variational Quantum Eigensolver (VQE) implemented on Qiskit’s quantum computer simulator, More >

Evren Daglarli^*

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4511-4537, 2025, DOI:10.32604/cmc.2025.065572 - 30 July 2025

Abstract This study aims to develop a quantum computing-based neurocognitive architecture that allows an agent to perform autonomous behaviors. Therefore, we present a brain-inspired cognitive architecture for autonomous agents that integrates a prefrontal cortex–inspired model with modern deep learning (a transformer-based reinforcement learning module) and quantum algorithms. In particular, our framework incorporates quantum computational routines (Deutsch–Jozsa, Bernstein–Vazirani, and Grover’s search) to enhance decision-making efficiency. As a novelty of this research, this comprehensive computational structure is empowered by quantum computing operations so that superiority in speed and robustness of learning compared to classical methods can be demonstrated.… More >

Kaleb Dias Antoine KODO^1,*, Eugène C. EZIN^1,2

Journal of Quantum Computing, Vol.7, pp. 17-38, 2025, DOI:10.32604/jqc.2025.059089 - 08 April 2025

Abstract In the rapidly evolving domain of quantum computing, Shor’s algorithm has emerged as a groundbreaking innovation with far-reaching implications for the field of cryptographic security. However, the efficacy of Shor’s algorithm hinges on the critical step of determining the period, a process that poses a substantial computational challenge. This article explores innovative quantum optimization solutions that aim to enhance the efficiency of Shor’s period finding algorithm. The article focuses on quantum development environments, such as Qiskit and Cirq. A detailed analysis is conducted on three notable tools: Qiskit Transpiler, BQSKit, and Mitiq. The performance of More >

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 >