标题： 深度生成模型中的量子潜在分布 显示英文标题

标题： Quantum latent distributions in deep generative models

摘要： 许多成功的生成模型家族利用了一个低维潜在分布，该分布被映射到数据分布。 尽管简单潜在分布常被使用，但已证明更复杂的分布可以提高性能。 例如，最近的研究探索了使用量子处理器产生的分布，并发现了经验上的改进。 然而，当由量子处理器产生的潜在空间分布可以预期提高性能，以及这些改进是否可重复，是开放性问题，我们在此工作中进行了研究。 我们证明，在某些条件下，这些“量子潜在分布”使生成模型能够产生经典潜在分布无法高效生成的数据分布。 我们还提供了可操作的直觉，以识别在现实场景中这种量子优势可能何时出现。 我们在一个合成量子数据集和QM9分子数据集上进行了基准测试实验，使用了模拟和真实的光子量子处理器。 我们的结果表明，与一系列经典基线相比，量子潜在分布可以在生成对抗网络中带来改进的生成性能。 我们还探讨了扩散和流匹配模型，确定了与量子潜在分布兼容的架构。 这项工作证实，近期的量子处理器可以扩展深度生成模型的能力。 显示英文摘要

摘要： Many successful families of generative models leverage a low-dimensional latent distribution that is mapped to a data distribution. Though simple latent distributions are commonly used, it has been shown that more sophisticated distributions can improve performance. For instance, recent work has explored using the distributions produced by quantum processors and found empirical improvements. However, when latent space distributions produced by quantum processors can be expected to improve performance, and whether these improvements are reproducible, are open questions that we investigate in this work. We prove that, under certain conditions, these "quantum latent distributions" enable generative models to produce data distributions that classical latent distributions cannot efficiently produce. We also provide actionable intuitions to identify when such quantum advantages may arise in real-world settings. We perform benchmarking experiments on both a synthetic quantum dataset and the QM9 molecular dataset, using both simulated and real photonic quantum processors. Our results demonstrate that quantum latent distributions can lead to improved generative performance in GANs compared to a range of classical baselines. We also explore diffusion and flow matching models, identifying architectures compatible with quantum latent distributions. This work confirms that near-term quantum processors can expand the capabilities of deep generative models.