TL;DR: QML explores whether quantum computers can accelerate machine learning tasks. Approaches include quantum kernel methods, variational quantum circuits as neural network layers, and quantum Boltzmann ma
QML explores whether quantum computers can accelerate machine learning tasks. Approaches include quantum kernel methods, variational quantum circuits as neural network layers, and quantum Boltzmann machines. While theoretical speedups exist for specific problems (HHL for linear systems), practical QML advantage remains unproven. Hybrid quantum-classical ML is the current focus.
Complexity
Varies - most QML algorithms lack proven speedup
Application
Classification, generative models, anomaly detection, reinforcement learning