In this work, we introduce the quantum-assisted Helmholtz machine: a hybrid quantum-classical framework with the potential of tackling high-dimensional real-world machine learning datasets on continuous variables. We use deep learning to extract a low-dimensional binary representation of data, suitable for processing on relatively small quantum computers. The quantum hardware and deep learning architecture work together to train an unsupervised generative model.

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