Quantum generative models are quantum algorithms designed to create new data samples that resemble an existing dataset. They leverage the principles of quantum mechanics to potentially outperform classical generative models by using quantum superposition and entanglement, enabling more efficient data representation and processing. These models have promising applications in various fields, including protein folding, where understanding complex molecular structures is crucial.
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