ON THE SAMPLE COMPLEXITY OF QUANTUM BOLTZMANN MACHINE LEARNING

On the sample complexity of quantum Boltzmann machine learning

Abstract Quantum Boltzmann machines (QBMs) are machine-learning models for both 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 10026145n size of the data set.By using the relative entropy as a loss function

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