Title: QUASAR: Quantum Assembly Code Generation Using Tool-Augmented LLMs via Agentic RL
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
Keywords: Quantum optimization algorithm, Quantum circuit generation, OpenQASM, Large language models, Agentic reinforcement learning

Abstract: Designing and optimizing task-specific quantum circuits are crucial to leverage the advantage of quantum computing.  Recent large language model (LLM)-based quantum circuit generation has emerged as a promising automatic solution. However, the fundamental challenges remain unaddressed: (i) parameterized quantum gates require precise numerical values for optimal performance, which also depend on multiple aspects, including the number of quantum gates, their parameters, and the layout/depth of the circuits. (ii) LLMs often generate low-quality or incorrect quantum circuits due to the lack of quantum domain-specific knowledge. We propose QUASAR, an agentic reinforcement learning (RL) framework for quantum circuits generation and optimization based on tool-augmented LLMs. To align the LLM with quantum-specific knowledge and improve the generated quantum circuits, QUASAR designs (i) a quantum circuit verification approach with external quantum simulators and (ii) a sophisticated hierarchical reward mechanism in RL training.  Extensive evaluation shows improvements in both syntax and semantic performance of the generated quantum circuits. 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 and several supervised-fine-tuning (SFT)-only and RL-only baselines.

TL;DR: We post-train large language models with agentic reinforcement learning and a hierarchical reward design to generate more syntactically correct and semantically faithful OpenQASM circuits for quantum optimization tasks.