Generative Adversarial Networks for Resource State Generation
This provides a scalable foundation for automated design of tailored quantum resources for information-processing applications, though it appears incremental as it builds on existing GAN methods with physics constraints.
The paper tackles quantum resource-state generation by introducing a physics-informed Generative Adversarial Network framework that learns to generate valid two-qubit states optimized for teleportation and entanglement broadcasting, achieving fidelities exceeding ~98% for theoretical resource boundaries.
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.