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 Gloves 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, t

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Review of ultrasound combinations with hybrid and innovative techniques for extraction and processing of food and natural products

Ultrasound has a significant effect on the rate of various processes in food, Cappuccino Cups perfume, cosmetic, pharmaceutical, bio-fuel, materials, or fine chemical industries, despite some shortcomings.Combination with other conventional or innovative techniques can overcome these limitations, enhance energy, momentum and mass transfer, and has

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Molecular de-novo design through deep reinforcement learning

Abstract This work introduces a method to tune a sequence-based generative model for molecular de novo design that through augmented episodic likelihood can learn to generate structures with certain specified desirable properties.We demonstrate how this model can execute a range of tasks such as generating analogues to a query structure and generat

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