Generative Adversarial Networks for Resource State Generation
Abstract
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.
Cite
@article{arxiv.2601.13708,
title = {Generative Adversarial Networks for Resource State Generation},
author = {Shahbaz Shaik and Sourav Chatterjee and Sayantan Pramanik and Indranil Chakrabarty},
journal= {arXiv preprint arXiv:2601.13708},
year = {2026}
}