---
base_model:
- Qwen/Qwen3-4B-Instruct-2507
datasets:
- Benyucong/graph-data-quantum-rl
language:
- en
library_name: transformers
license: apache-2.0
metrics:
- code_eval
pipeline_tag: text-generation
tags:
- agent
- code
- QASM
- quantum
---

# QUASAR: Quantum Assembly Code Generation with Tool-Augmented RL

[![Paper](https://img.shields.io/badge/Paper-2510.00967-B31B1B?logo=arxiv)](https://huggingface.co/papers/2510.00967) [![Code](https://img.shields.io/badge/GitHub-Code-181717?logo=github)](https://github.com/benyucong/QUASAR) [![Dataset](https://img.shields.io/badge/Dataset-Benyucong%2Fgraph--data--quantum--rl-orange)](https://huggingface.co/datasets/Benyucong/graph-data-quantum-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  

---

## Model Details

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

---

## 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  

---

## 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.  

---

## How to Get Started

```python
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))
```

---

## Training Details

### Training Data
- Dataset: [Benyucong/graph-data-quantum-rl](https://huggingface.co/datasets/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](https://huggingface.co/Benyucong/sft_quantum_circuit_gen_4B).  
- **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  

---

## 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)

| Method              | Pass@1 SCR ↑ | Pass@1 SREV ↑ | Pass@1 RE ↓ | Pass@1 HQCR ↑ | Pass@10 SCR ↑ | Pass@10 SREV ↑ | Pass@10 RE ↓ | Pass@10 HQCR ↑ |
|---------------------|--------------|---------------|-------------|---------------|---------------|----------------|--------------|----------------|
| DeepSeek-V3         | 94.83%       | 12.24%        | 19.20       | 10.00%        | 98.97%        | 26.38%         | 16.39        | 16.38%         |
| GPT-5               | 87.07%       | 10.00%        | 19.94       | 6.90%         | 90.52%        | 27.07%         | 11.57        | 16.55%         |
| GPT-4o              | 87.93%       | 9.83%         | 19.42       | 6.38%         | 88.79%        | 18.62%         | 14.08        | 12.07%         |
| **Qwen3-4B SFT**    | 97.41%       | 18.97%        | 12.74       | 15.17%        | 99.65%        | 31.55%         | 10.81        | 23.62%         |
| Cold Start GRPO     | 84.48%       | 19.84%        | 14.32       | 12.41%        | 95.17%        | 27.59%         | 11.38        | 18.96%         |
| **QUASAR (ours)**   | **99.31%**   | **22.41%**    | **11.61**   | **17.24%**    | **100%**      | **33.10%**     | **8.48**     | **27.24%**     |

---

## Environmental Impact
- **Hardware Type:** NVIDIA H100 (16×, 64GB)  
- **Training Hours:** ~48  

---

## 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  

---

## Citation
```bibtex
@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}, 
}
```