racineai/VDR_Quantum_Circuit_Synthetic

---
license: apache-2.0
language:
- en
tags:
- RAG
- quantum
- quantum circuit
- synthetic
- physics
- DSE
task_categories:
- visual-document-retrieval
- text-retrieval
---
# VDR_Quantum_Circuit_Synthetic – Overview

**VDR_Quantum_Circuit_Synthetic** is a curated multimodal dataset focused on synthetic quantum circuits. It combines generated circuit images with expert-level technical queries 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/tree/main)**, adapted to handle synthetic data.

Quantum circuit images and descriptions were generated programmatically using **[Qiskit’s standard gate library](https://quantum.cloud.ibm.com/docs/en/api/qiskit/circuit_library#standard-gates)**. These were then used in a custom pipeline to produce technical queries aligned with each circuit using **Google’s Gemini 2.5 Pro model**.

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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 a quantum circuit
- `image`: A visual rendering of the circuit diagram
- `language`: The detected language of the query

Each synthetic circuit 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 for quantum circuits
- Exploring synthetic quantum circuits as a use case in AI workflows

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## Authors

- **Yumeng Ye**
- **Léo Appourchaux**

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