Quantum Boltzmann Machines are a type of quantum generative model that leverages quantum mechanics to simulate probability distributions and generate complex data. They extend classical Boltzmann machines by utilizing quantum states and entanglement, enabling them to capture correlations in data more efficiently than their classical counterparts. This approach opens new avenues for learning from large datasets in various applications such as image generation, natural language processing, and drug discovery.
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