RL-Quantum-4B-GGUF

QUASAR is a 4B-parameter language model fine-tuned from Qwen3-4B-Instruct-2507 through supervised learning followed by agentic reinforcement learning with tool-augmented feedback. Specially designed for generating OpenQASM 3.0 quantum circuits for tasks like QAOA and VQE, the model optimizes for both syntactic validity and semantic fidelity, using external quantum simulation for reward calculation across hierarchical criteria (syntax, distribution alignment, expectation value, and optimization progress). QUASAR is best suited for natural language to quantum circuit generation and quantum optimization algorithm design in research or integration scenarios, though users are advised to validate its outputs using external quantum simulators to address limitations in problem generalization. Training used a dataset with QASM 3.0 circuits and quantum optimization problems, employing SFT and RL (with GRPO and hierarchical reward). In evaluations, the model substantially outperforms comparable baselines—achieving leading results in syntactic correctness, distributional alignment, expectation-value matching, and high-quality circuit yield in both Pass@1 and Pass@10 metrics.

Model Files

Quants Usage