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Quantum Assistant: Multimodal VLMs for Quantum Computing
I've open-sourced my undergraduate thesis work on specializing vision-language models for quantum computing with Qiskit.
Existing quantum code assistants (like IBM's Qiskit Code Assistant
Qiskit ) only process text, ignoring visual representations—circuit diagrams, Bloch spheres, histograms
What I built:
- A synthetic data generation pipeline that extracts content from Qiskit documentation, papers, codes transcribes images via VLM, generates validate input and output pairs, and validates all code through automated unit tests
- The first public multimodal dataset for quantum computing: 8,366 samples (45% with images) across function completion, code generation, and Q&A tasks
- Fine-tuned Qwen3-VL-8B using LoRA (rsLoRA r=32), achieving +11pp on Qiskit HumanEval (32.45% → 43.71%) and +17.9pp on multimodal samples vs text-only
- Interactive demo with chat interface and code challenges
Results: The model achieves 63.39% Pass@1 on visual samples—it learned to extract circuit topology from diagrams and infer parameters from visual annotations.
Everything is Apache 2.0:
- Dataset: samuellimabraz/quantum-assistant
- Models: https://huggingface.co/collections/samuellimabraz/quantum-assistant
- Code & Pipeline: https://github.com/samuellimabraz/quantum-assistant
- Demo: samuellimabraz/quantum-assistant
The synthetic pipeline is modular and can be adapted for other technical domains.
This work was inspired by the Qiskit team's work on code generation ([arXiv:2405.19495](https://arxiv.org/abs/2405.19495)) by @cbjuan @ndupuis
Built with ms-swift, transformers, vLLM, PEFT, and Qiskit. grateful for the open-source ecosystem that makes projects like this possible.
I've open-sourced my undergraduate thesis work on specializing vision-language models for quantum computing with Qiskit.
Existing quantum code assistants (like IBM's Qiskit Code Assistant
What I built:
- A synthetic data generation pipeline that extracts content from Qiskit documentation, papers, codes transcribes images via VLM, generates validate input and output pairs, and validates all code through automated unit tests
- The first public multimodal dataset for quantum computing: 8,366 samples (45% with images) across function completion, code generation, and Q&A tasks
- Fine-tuned Qwen3-VL-8B using LoRA (rsLoRA r=32), achieving +11pp on Qiskit HumanEval (32.45% → 43.71%) and +17.9pp on multimodal samples vs text-only
- Interactive demo with chat interface and code challenges
Results: The model achieves 63.39% Pass@1 on visual samples—it learned to extract circuit topology from diagrams and infer parameters from visual annotations.
Everything is Apache 2.0:
- Dataset: samuellimabraz/quantum-assistant
- Models: https://huggingface.co/collections/samuellimabraz/quantum-assistant
- Code & Pipeline: https://github.com/samuellimabraz/quantum-assistant
- Demo: samuellimabraz/quantum-assistant
The synthetic pipeline is modular and can be adapted for other technical domains.
This work was inspired by the Qiskit team's work on code generation ([arXiv:2405.19495](https://arxiv.org/abs/2405.19495)) by @cbjuan @ndupuis
Built with ms-swift, transformers, vLLM, PEFT, and Qiskit. grateful for the open-source ecosystem that makes projects like this possible.