Quantum computing has rapidly progressed from its theoretical foundations in the 1980s to today’s hardware prototypes capable of operating with hundreds of qubits. Despite being in its early stages, these advances in both hardware and algorithms have opened the door to practical applications. In particular, Noisy Intermediate-Scale Quantum (NISQ) devices show promise in tackling problems where classical systems struggle. Among the most impactful approaches are Variational Quantum Algorithms (VQAs) such as the Variational Quantum Eigensolver (VQE), which are resilient to noise and optimized for limited qubit resources. Beyond algorithmic innovation, Hamiltonian simulations provide new ways to model complex dynamics, offering transformative opportunities across science and engineering. Together, these developments highlight the potential of quantum computing to revolutionize signal processing, machine learning, and artificial intelligence, setting the stage for both groundbreaking research and real-world applications.

This Special Session welcomes submissions that explore interdisciplinary innovations and address emerging challenges in neural networks and computational intelligence through quantum computing methodologies. With a particular emphasis on Variational Quantum Circuits (VQCs) and related approaches, the session seeks to accelerate the development of next-generation AI-driven solutions. We invite contributions on algorithms and applications that harness the computational advantages of quantum systems to tackle complex problems in machine learning, reinforcement learning, multi-agent systems, signal processing, natural language processing, as well as industrial and scientific domains.

Topics