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Quantum Boltzmann machines (QBMs) are machine-learning models for both classical and quantum data. These models use the relative entropy as a loss function and can be solved with stochastic gradient descent. Pre-training strategies can also be used to lower the sample complexity bounds. QBMs have been verified numerically and show promise as machine learning models.
