量子生成模型在68量子比特上实现经典难以模拟的分布学习，展现可证明优势。
Generative quantum advantage for classical and quantum problems
- 设计新型量子生成模型，克服训练困难与局部极小问题。
- 在68量子比特处理器上成功学习经典难模拟的概率分布。
- 适用于加速物理模拟，适合追求量子优势的研究者。
生成式机器学习的最新突破得益于庞大的计算资源，展现出类人能力。尽管超越经典能力的量子实验能生成经典难以计算的分布样本，其复杂性阻碍了高效学习。这使得生成式量子优势——即量子计算机比经典计算机更优地学习并生成目标输出——难以实现。本文通过引入一类难以经典模拟、可高效训练、无梯度消失和局部极小泛滥的生成量子模型，解决了该挑战。利用68量子比特超导量子处理器，在两类场景中验证：学习经典难以处理的概率分布，以及学习用于加速物理模拟的量子电路。结果表明，超越经典区域的学习与采样均可高效完成，为具有可证明优势的量子增强生成模型开辟新路径。
原文摘要 · Abstract (English)
Recent breakthroughs in generative machine learning, powered by massive computational resources, have demonstrated unprecedented human-like capabilities. While beyond-classical quantum experiments can generate samples from classically intractable distributions, their complexity has thwarted all efforts toward efficient learning. This challenge has hindered demonstrations of generative quantum advantage: the ability of quantum computers to learn and generate desired outputs substantially better than classical computers. We resolve this challenge by introducing families of generative quantum models that are hard to simulate classically, are efficiently trainable, exhibit no barren plateaus or proliferating local minima, and can learn to generate distributions beyond the reach of classical computers. Using a $68$-qubit superconducting quantum processor, we demonstrate these capabilities in two scenarios: learning classically intractable probability distributions and learning quantum circuits for accelerated physical simulation. Our results establish that both learning and sampling can be performed efficiently in the beyond-classical regime, opening new possibilities for quantum-enhanced generative models with provable advantage.
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