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

• URL: http://arxiv.org/abs/2510.00967v1
• Date: Wed, 01 Oct 2025 14:40:04 GMT
• Title: QUASAR: Quantum Assembly Code Generation Using Tool-Augmented LLMs via Agentic RL
• Authors: Cong Yu, Valter Uotila, Shilong Deng, Qingyuan Wu, Tuo Shi, Songlin Jiang, Lei You, Bo Zhao,
• Abstract summary: Large language model (LLM)-based quantum circuit generation has emerged as a promising automatic solution.<n>We propose QUASAR, an agentic reinforcement learning framework for quantum circuits generation and optimization.
• Score: 8.823588193058727
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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