Generative Adversarial Networks for Resource State Generation

• URL: http://arxiv.org/abs/2601.13708v1
• Date: Tue, 20 Jan 2026 08:04:57 GMT
• Title: Generative Adversarial Networks for Resource State Generation
• Authors: Shahbaz Shaik, Sourav Chatterjee, Sayantan Pramanik, Indranil Chakrabarty,
• Abstract summary: We introduce a physics-informed Generative Adversarial Network framework that recasts quantum resource-state generation as an inverse-design task.<n>By embedding task-specific utility functions into training, the model learns to generate valid two-qubit states optimized for teleportation and entanglement broadcasting.
• Score: 1.9832598896178517
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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