---
title: QuantumLLMInstruct
emoji: 🦀
colorFrom: green
colorTo: indigo
sdk: docker
pinned: false
short_description: 'QuantumLLMInstruct: A 500k LLM Instruction-Tuning Dataset'
---

# QuantumLLMInstruct: A 500k LLM Instruction-Tuning Dataset with Problem-Solution Pairs for Quantum Computing

### Dataset Overview

**QuantumLLMInstruct (QLMMI)** is a groundbreaking dataset designed to fine-tune and evaluate Large Language Models (LLMs) in the domain of quantum computing. This dataset spans **90 primary quantum computing domains** and contains over **500,000 rigorously curated instruction-following problem-solution pairs**. 

The dataset focuses on enhancing reasoning capabilities in LLMs for quantum-specific tasks, including Hamiltonian dynamics, quantum circuit optimization, and Yang-Baxter solvability. 

Each entry consists of:
- A **quantum computing problem** expressed in natural language and/or LaTeX.
- A detailed **step-by-step solution**, designed for precision and clarity.
- Domain-specific metadata, such as the problem's **main domain**, **sub-domain**, and associated tags.

![QuantumLLMInstruct Workflow](qlmmi-detailed-flowchart.jpg)
---

### Data Sources

The dataset leverages cutting-edge methodologies to generate problems and solutions:
1. **Predefined Templates**: Problems crafted using robust templates to ensure domain specificity and mathematical rigor.
2. **LLM-Generated Problems**: Models such as `Qwen-2.5-Coder` autonomously generate complex problems across diverse quantum topics, including:
   - Synthetic Hamiltonians
   - QASM code
   - Jordan-Wigner transformations
   - Trotter-Suzuki decompositions
   - Quantum phase estimation
   - Variational Quantum Eigensolvers (VQE)
   - Gibbs state preparation
3. **Advanced Reasoning Techniques**: Leveraging Chain-of-Thought (CoT) and Task-Oriented Reasoning and Action (ToRA) frameworks to refine problem-solution pairs.

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### Structure

The dataset contains the following fields:
- `images`: Optional multimodal inputs, such as visualizations of quantum circuits or spin models.
- `problem_text`: The quantum computing problem, formatted in plain text or LaTeX.
- `solution`: A detailed solution generated by state-of-the-art LLMs.
- `main_domain`: The primary quantum domain, e.g., "Quantum Spin Chains" or "Hamiltonian Dynamics."
- `sub_domain`: Specific subtopics, e.g., "Ising Models" or "Trotterization."
- `tags`: Relevant tags for classification and retrieval.
- `model_name`: The name of the model used to generate the problem or solution.
- `timestamp`: The date and time of creation.

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### Key Features

- **Comprehensive Coverage**: Spanning 90 primary domains and hundreds of subdomains.
- **High Quality**: Problems and solutions validated through advanced reasoning frameworks and Judge LLMs.
- **Open Access**: Designed to support researchers, educators, and developers in the field of quantum computing.
- **Scalable Infrastructure**: Metadata and structure optimized for efficient querying and usage.

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### Example Domains

Some of the key domains covered in the dataset include:
- Synthetic Hamiltonians: Energy computations and time evolution.
- Quantum Spin Chains: Ising, Heisenberg, and advanced integrable models.
- Yang-Baxter Solvability: Solving for quantum integrable models.
- Trotter-Suzuki Decompositions: Efficient simulation of Hamiltonian dynamics.
- Quantum Phase Estimation: Foundational in quantum algorithms.
- Variational Quantum Eigensolvers (VQE): Optimization for quantum chemistry.
- Randomized Circuit Optimization: Enhancing algorithm robustness in noisy conditions.
- Quantum Thermodynamics: Gibbs state preparation and entropy calculations.

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### Contributions

This dataset represents a collaborative effort to advance quantum computing research through the use of large-scale LLMs. It offers:
1. A scalable and comprehensive dataset for fine-tuning LLMs.
2. Rigorous methodologies for generating and validating quantum problem-solving tasks.
3. Open-access resources to foster collaboration and innovation in the quantum computing community.

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Cite:
@dataset{quantumllm_instruct,
  title={QuantumLLMInstruct: A 500k LLM Instruction-Tuning Dataset with Problem-Solution Pairs for Quantum Computing},
  author={Shlomo Kashani},
  year={2025},
  url={https://huggingface.co/datasets/QuantumLLMInstruct}
}