Learning compressed, latent representations of quantum states can help us understand and simulate the natural world. To this end, one needs devices that can capture and reliably process information using quantum-mechanical effects. Whilst we currently lack a device that can achieve this goal, we are well into the era of noisy, intermediate-scale quantum (NISQ) computers—quantum machines with \(50{-}100\) noisy qubits—which have catalysed machine learning applications. This chapter provides the main ideas underpinning learning with NISQ machines. It also provides insight into the architectures and training schemes currently in use, as well as the limitations and potential futures of the field.