QUASAR: Quantum Assembly Code Generation Using Tool-Augmented LLMs via Agentic RL
Published in arXiv preprint, 2025
Designing and optimizing task-specific quantum circuits are crucial to leverage the advantage of quantum computing. We propose QUASAR, an agentic reinforcement learning (RL) framework for quantum circuits generation and optimization based on tool-augmented LLMs. When augmenting a 4B LLM, QUASAR has achieved the validity of 99.31% in Pass@1 and 100% in Pass@10, outperforming industrial LLMs of GPT-4o, GPT-5 and DeepSeek-V3.