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The article discusses the use of generative models, specifically diffusion models, in quantum computing. These models are able to generate quantum circuits based on text descriptions, making it easier to prepare quantum states and execute algorithms on quantum computers. The models are trained in a unique way that avoids the need for simulating quantum circuits, providing a significant advantage. The article also highlights the flexibility of these models in generating circuits with different numbers and types of quantum gates, as well as taking into consideration the connectivity of the quantum hardware.
