On the sample complexity of quantum Boltzmann machine learning
Abstract Quantum Boltzmann machines (QBMs) are machine-learning models for both Foot Pad classical and quantum data.We give an operational definition of QBM learning in terms of the difference in expectation values between the model and target, taking into account the polynomial size of the data set.By using the relative entropy as a loss function,