Quantum Transfer Learning

Quantum transfer learning combines classical machine learning's feature extraction capabilities with quantum computing's potential for enhanced efficiency and generalization, aiming to improve the performance of various classification tasks. Current research focuses on hybrid quantum-classical models, often employing variational quantum circuits or quantum Boltzmann machines integrated with pre-trained classical networks like ResNets or convolutional neural networks, applied to diverse domains such as image classification (including medical imaging) and natural language processing. This approach shows promise for improving accuracy and efficiency in specific applications, though current results often demonstrate only comparable or slightly better performance than purely classical methods, highlighting the need for further algorithmic and hardware advancements.