Generative Adversarial Networks for Resource State Generation

TL;DR

This paper presents a physics-informed GAN framework for generating quantum resource states, optimizing for specific tasks like teleportation, with high fidelity and improved training stability, advancing automated quantum resource design.

Contribution

It introduces a novel physics-informed GAN approach that incorporates task-specific utility functions and structural constraints for efficient quantum resource state generation.

Findings

Achieves over 98% fidelity in generating Werner-like and Bell-diagonal states.

Reproduces theoretical resource boundaries for quantum states.

Demonstrates improved training stability over loss-only methods.

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.