quantum machine learning

A research field at the intersection of quantum computing and machine learningmachine learning, n.: A way of building software that learns from examples instead of following rules written by a programmer. Shown thousands of labeled photos, for instance, a program can learn to recognize cats., with several strands: quantum algorithms intended to speed up learning on classical data (such as linear-algebra subroutines derived from the HHL algorithm); variational models, or 'quantum neural networks', in which parameterized quantum circuits are trained by a classical optimizer; learning from quantum data, such as states produced by quantum sensors or experiments, where provable advantages are clearest; and classical machine learning applied to quantum physics and hardware control. Several claimed exponential speedups have been weakened by 'dequantization' — classical algorithms with comparable performance under the same data-access assumptions — while variational models face barren plateaus (vanishing gradients) and noise on current hardware. As of 2026 no practically useful quantum advantage for mainstream machine learning has been demonstrated.