rl_quantum_4b

QUASAR: Quantum Assembly Code Generation with Tool-Augmented RL

Model Summary

QUASAR is a 4B-parameter model fine-tuned from Qwen3-4B-Instruct-2507 using a two-stage process: supervised fine-tuning (SFT) followed by agentic reinforcement learning (RL) with tool-augmented feedback.

The model is designed to generate OpenQASM 3.0 quantum circuits for optimization problems such as QAOA and VQE, achieving high syntactic validity and semantic fidelity.

• Framework: Agentic RL with external quantum simulator verification
• Reward: Hierarchical 4-level reward (syntax, distribution alignment, expectation value, optimization progress)
• Primary Domain: Quantum circuit generation and quantum optimization algorithm design

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Model Details

• Model type: LLM fine-tuned with reinforcement learning
• Languages: English
• License: Apache-2.0
• Base model: Qwen/Qwen3-4B-Instruct-2507

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Uses

Direct Use

• Generate OpenQASM 3.0 code from natural language descriptions
• Design ansatz circuits for quantum optimization tasks (QAOA, VQE)

Downstream Use

• Integration into quantum compilers
• Research on LLM-guided quantum algorithm design

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Bias, Risks, and Limitations

• May produce a valid QASM that is semantically weak if prompts are ambiguous
• Tailored primarily to graph-based quantum optimization problems
• Evaluated mainly in simulation; hardware generalization remains untested

Recommendation: Always verify generated circuits with independent quantum simulators or compilers before deployment.

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How to Get Started

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("Benyucong/rl_quantum_4b")
tokenizer = AutoTokenizer.from_pretrained("Benyucong/rl_quantum_4b")

prompt = """Design a QASM 3.0 quantum circuit with 3 qubits and 3 layers to solve the vertex_cover \
given the graph: {"directed": false, "multigraph": false, "graph": {}, "nodes": [{"id": 0}, {"id": 1}, {"id": 2}], \
"edges": [{"source": 0, "target": 1}, {"source": 0, "target": 2}, {"source": 1, "target": 2}]}. \
Provide valid QASM 3.0 code with optimal parameters."""

inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=1024)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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Training Details

Training Data

• Dataset: Benyucong/graph-data-quantum-rl
• Contains QASM 3.0 circuits, Hamiltonians, eigenvalues, and parameterized circuits for 12 quantum optimization problems

Training Setup

• Stage 1: Supervised fine-tuning (SFT), the model is available here.
• Stage 2: Reinforcement learning with GRPO and hierarchical reward

Hyperparameters

• Batch size: 128
• Rollouts: 16 per prompt (temperature = 0.7, top-p = 0.8)
• Precision: bf16 mixed precision
• GPUs: 16 × H100-64GB (FSDP enabled)
• Training time: ~48 hours

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Evaluation

Metrics (Please check our paper for details)

• SCR: Syntactic Correctness Ratio
• SREV: Successful Rate of Expectation Value
• RE: Relative Entropy (distributional alignment)
• HQCR: High-Quality Circuit Ratio

Results (QUASAR vs Baselines)

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Environmental Impact

• Hardware Type: NVIDIA H100 (16×, 64GB)
• Training Hours: ~48

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Technical Specifications

• Architecture: Qwen3-4B-Instruct-2507
• Fine-tuning: SFT + RL (GRPO)
• Reward Design: Syntax validity, distributional alignment (JS distance), expectation-value matching, optimization-progress efficiency
• Frameworks: PyTorch, vLLM, Qiskit, OpenQASM

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Citation

@misc{yu2025quasarquantumassemblycode,
      title={QUASAR: Quantum Assembly Code Generation Using Tool-Augmented LLMs via Agentic RL}, 
      author={Cong Yu and Valter Uotila and Shilong Deng and Qingyuan Wu and Tuo Shi and Songlin Jiang and Lei You and Bo Zhao},
      year={2025},
      eprint={2510.00967},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2510.00967}, 
}