Scope

Deadline Date: 30 January 2026

The ability to learn brain connectivity is generally lacking in standard deep learning models used for vision-brain understanding. Quantum computing, however, introduces a new paradigm for model development. This special issue proposes a novel Quantum-Brain approach—a quantum-inspired neural network influenced by both brain connectivity and quantum entanglement. Quantum technologies, including those used in electrical, optical, and medical devices, are advancing rapidly, particularly in communication and computation. Yet, access remains limited to high-tech organizations. To bridge this gap, quantum-inspired metaheuristics combine quantum principles with metaheuristic algorithms, enhancing global search through probabilistic quantum bit representations. These algorithms are valuable in solving complex optimization problems in various industries. Machine learning, especially neural networks, offers highly efficient and accurate function approximation in high-dimensional spaces, opening new possibilities in AI and scientific computing. Quantum computing (QC), drawing from physics, mathematics, and computer science, is attracting significant attention across academia and industry. Alongside exascale systems, neuromorphic hardware (NMH) and QC are transforming high-performance computing, especially in areas like biomolecular simulations. This special issue aims to explore the development, classification, and practical applications of quantum-inspired metaheuristic algorithms, highlighting their role in solving real-world scientific and engineering problems using next-generation computational technologies.