Quantum generative models are frameworks that leverage quantum mechanics to generate data samples from a specific probability distribution, enabling the simulation of complex systems. These models aim to capture and reproduce the underlying data distribution by using quantum states and operations, which can potentially outperform classical generative models in terms of efficiency and capability. By integrating quantum properties like superposition and entanglement, these models have unique applications in areas like state preparation, implementation case studies, and the synergy between quantum machine learning and classical AI systems.
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