Deep Quantum

Deep quantum research explores the integration of quantum computing principles into deep learning models, aiming to enhance the capabilities of artificial intelligence. Current efforts focus on developing hybrid quantum-classical neural networks, employing architectures like variational quantum circuits and quantum convolutional layers within classical frameworks, as well as investigating the use of quantum annealing for optimization problems. This field is significant for its potential to improve the efficiency and performance of machine learning algorithms in various applications, including medical imaging, materials science, and drug discovery, particularly where classical methods struggle with high dimensionality or computational complexity. The ultimate goal is to leverage quantum mechanics to overcome limitations of classical deep learning.