用对抗生成网络自动设计高保真量子态，提升量子通信效率。
Generative Adversarial Networks for Resource State Generation
- 将量子态生成转为逆向设计，嵌入物理约束优化生成质量。
- 生成的两比特态在纠缠传输任务中保真度超98%，接近理论极限。
- 适合量子信息处理与网络设计领域，可快速定制专用资源态。
我们提出一种融合物理先验的生成对抗网络框架，将量子资源态生成转化为逆向设计问题。通过在训练中嵌入任务特异性效用函数，模型学习生成适用于量子隐形传态与纠缠广播的有效两比特态。对比基于分解与直接生成的架构发现，对厄米性、迹为一和正定性进行结构强制比仅靠损失函数约束能获得更高保真度与训练稳定性。该框架在威纳类与贝尔对角态上复现了理论资源边界，保真度超过~98%，确立对抗学习在约束驱动的量子态发现中的轻量高效性。该方法为信息处理应用中定制化量子资源的自动化设计提供了可扩展基础，以隐形传态与纠缠广播为例，并为高效量子网络设计中使用此类态开辟了可能。
原文摘要 · Abstract (English)
We introduce a physics-informed Generative Adversarial Network framework that recasts quantum resource-state generation as an inverse-design task. By embedding task-specific utility functions into training, the model learns to generate valid two-qubit states optimized for teleportation and entanglement broadcasting. Comparing decomposition-based and direct-generation architectures reveals that structural enforcement of Hermiticity, trace-one, and positivity yields higher fidelity and training stability than loss-only approaches. The framework reproduces theoretical resource boundaries for Werner-like and Bell-diagonal states with fidelities exceeding ~98%, establishing adversarial learning as a lightweight yet effective method for constraint-driven quantum-state discovery. This approach provides a scalable foundation for automated design of tailored quantum resources for information-processing applications, exemplified with teleportation and broadcasting of entanglement, and it opens up the possibility of using such states in efficient quantum network design.
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