QUASAR: Quantum Assembly Code Generation Using Tool-Augmented LLMs via
  Agentic RL
Authors: name: Cong Yu, name: Valter Uotila, name: Shilong Deng, name: Qingyuan Wu, name: Tuo Shi, name: Songlin Jiang, name: Lei You, name: Bo Zhao

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.