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Machine learning has been presented as one of the key applications fornear-term quantum technologies, given its high commercial value and wide rangeof applicability. In this work, we introduce the \textit{quantum-assistedHelmholtz machine:} a hybrid quantum-classical framework with the potential oftackling high-dimensional real-world machine learning datasets on continuousvariables. Instead of using quantum computers only to assist deep learning, asprevious approaches have suggested, we use deep learning to extract alow-dimensional binary representation of data, suitable for processing onrelatively small quantum computers. Then, the quantum hardware and deeplearning architecture work together to train an unsupervised generative model.We demonstrate this concept using 1644 quantum bits of a D-Wave 2000Q quantumdevice to model a sub-sampled version of the MNIST handwritten digit datasetwith 16x16 continuous valued pixels. Although we illustrate this concept on aquantum annealer, adaptations to other quantum platforms, such as ion-traptechnologies or superconducting gate-model architectures, could be exploredwithin this flexible framework.

Quantum-assisted Helmholtz machines: A quantum-classical deep learning framework for industrial datasets in near-term devices