Quantum Machine Learning

Quantum machine learning studies how quantum states, measurements, and computation can support learning from classical or quantum data for tasks such as prediction, generation, sensing, and decision-making. Common approaches include variational quantum circuits and hybrid neural networks, quantum kernels and Gaussian processes, generative models, and learning-theoretic tools such as classical shadows and PAC bounds. Researchers test these methods on image and time-series tasks, molecular prediction, quantum-state and many-body dynamics learning, and reinforcement learning, while addressing data encoding, measurement cost, noise, trainability, and generalization. Evidence remains mixed: particular structured or quantum-data problems may benefit, but broad computational advantage over carefully tuned classical methods is not established and depends on fair, resource-matched evaluation.