This research introduces a novel approach that leverages Large Language Models to improve Quantum Generative Adversarial Networks through optimized ansatz design.

• Iteratively refines quantum circuit structures to improve accuracy
• Reduces circuit depth and number of parameters for more efficient quantum models
• Creates a feedback loop where LLMs analyze results and suggest improvements
• Establishes a framework for AI-driven quantum algorithm development

This advancement is significant for quantum engineering as it addresses the critical challenge of optimizing quantum circuits - a fundamental bottleneck in practical quantum computing applications.

Optimizing Ansatz Design in Quantum Generative Adversarial Networks Using Large Language Models