---
title: OGC_Quantum
canonical_url: "https://www.modelscope.cn/datasets/racineai/OGC_Quantum"
md_url: "https://www.modelscope.cn/datasets/racineai/OGC_Quantum.md"
repository: racineai/OGC_Quantum
last_updated: 2025-11-26
license: apache-2.0
storage_size: "74 GB"
downloads: 1742
stars: 0
---

# OGC_Quantum

> OGC_Quantum - racineai 在 ModelScope 开源的数据集。VDRQuantum – Overview

racineai/OGC_Quantum 是 ModelScope 魔搭社区上的数据集，存储大小 74 GB，采用 apache-2.0 许可。

- **Repository**: racineai/OGC_Quantum
- **License**: apache-2.0
- **Storage size**: 74 GB
- **Downloads**: 1742
- **Stars**: 0
- **Last updated**: 2025-11-26

Source: https://www.modelscope.cn/datasets/racineai/OGC_Quantum

---

# VDR_Quantum – Overview

**VDR_Quantum** is a curated multimodal dataset focused on **quantum technical documents**. It combines text and image data extracted from real scientific PDFs to support tasks such as RAG DSE, question answering, document search, and vision-language model training.

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## Dataset Composition

This dataset was created using our open-source tool [VDR_pdf-to-parquet](https://github.com/RacineAIOS/VDR_pdf-to-parquet).  
Quantum-related PDFs were collected from public online sources. Each document was processed page-by-page to extract text, convert pages into high-resolution images, and generate synthetic technical queries.

We used **Google’s Gemini 2.0 Flash Lite** model in a custom pipeline to generate diverse, expert-level questions that align with the content of each page.

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## Dataset Structure

Each entry in the dataset contains:

- `id`: A unique identifier for the sample
- `query`: A synthetic technical question generated from that page
- `image`: A visual rendering of a PDF page    
- `language`: The detected language of the query  

> Each page produces 4 unique entries: a main technical query, a secondary one, a visual-based question, and a multimodal semantic query.

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## Purpose

This dataset is designed to support:

- Training and evaluating **vision-language models** 
- Developing **multimodal search or retrieval systems**  
- Research in **automated question generation** from technical sources  
- Enhancing tools for **quantum document analysis and understanding**

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## Authors

- **Yumeng Ye**
- **Léo Appourchaux** 

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