QUASAR: Quantum Assembly Code Generation Using Tool-Augmented LLMs via Agentic RL

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. Extensive evaluation shows improvements in both syntax and semantic performance of the generated quantum circuits. We release our model at HuggingFace and provide the training code at GitHub. Quantum hardware has improved remarkably in recent years (AI & Collaborators, 2025; Bravyi et al., 2024; Bluvstein et al., 2024) and this rapid hardware development creates demand for improved quantum software and algorithms. Quantum software and algorithms can be categorized into classical platforms that support quantum computers themselves, including quantum error mitigation software and quantum compilers. The second category comprises domain-specific quantum algorithms, including examples like Shor's algorithm and Grover's algorithm. At the core of quantum software and algorithms is the quantum circuit model (Nielsen & Chuang, 2010), which is an assembly-level abstraction for operating gate-based quantum computers. Most of the quantum algorithms can be expressed as quantum circuits (Jordan, 2025). The design of quantum circuits is the foundation in quantum compilers and quantum algorithm development.