Quantum Architecture Search

Quantum architecture search (QAS) automates the design of efficient quantum circuits, addressing the significant challenge of creating effective quantum algorithms for near-term quantum computers. Current research heavily utilizes machine learning techniques, particularly reinforcement learning and evolutionary algorithms, often employing variational quantum circuits (VQCs) and exploring novel neural network architectures like Kolmogorov-Arnold Networks to optimize circuit structure and minimize gate count. Successful QAS methods promise to accelerate the development of quantum algorithms and applications by reducing the need for expert-level design, ultimately advancing the field of quantum computing and its practical impact.