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--- |
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title: QuantumLLMInstruct |
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short_description: 'QuantumLLMInstruct: A 500k LLM Instruction-Tuning Dataset' |
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--- |
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# QuantumLLMInstruct: A 500k LLM Instruction-Tuning Dataset with Problem-Solution Pairs for Quantum Computing |
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### Dataset Overview |
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**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**. |
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The dataset focuses on enhancing reasoning capabilities in LLMs for quantum-specific tasks, including Hamiltonian dynamics, quantum circuit optimization, and Yang-Baxter solvability. |
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Each entry consists of: |
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- A **quantum computing problem** expressed in natural language and/or LaTeX. |
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- A detailed **step-by-step solution**, designed for precision and clarity. |
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- Domain-specific metadata, such as the problem's **main domain**, **sub-domain**, and associated tags. |
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### Data Sources |
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The dataset leverages cutting-edge methodologies to generate problems and solutions: |
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1. **Predefined Templates**: Problems crafted using robust templates to ensure domain specificity and mathematical rigor. |
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2. **LLM-Generated Problems**: Models such as `Qwen-2.5-Coder` autonomously generate complex problems across diverse quantum topics, including: |
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- Synthetic Hamiltonians |
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- QASM code |
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- Jordan-Wigner transformations |
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- Trotter-Suzuki decompositions |
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- Quantum phase estimation |
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- Variational Quantum Eigensolvers (VQE) |
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- Gibbs state preparation |
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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 |
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The dataset contains the following fields: |
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- `images`: Optional multimodal inputs, such as visualizations of quantum circuits or spin models. |
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- `problem_text`: The quantum computing problem, formatted in plain text or LaTeX. |
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- `solution`: A detailed solution generated by state-of-the-art LLMs. |
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- `main_domain`: The primary quantum domain, e.g., "Quantum Spin Chains" or "Hamiltonian Dynamics." |
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- `sub_domain`: Specific subtopics, e.g., "Ising Models" or "Trotterization." |
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- `tags`: Relevant tags for classification and retrieval. |
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- `model_name`: The name of the model used to generate the problem or solution. |
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- `timestamp`: The date and time of creation. |
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### Key Features |
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- **Comprehensive Coverage**: Spanning 90 primary domains and hundreds of subdomains. |
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- **High Quality**: Problems and solutions validated through advanced reasoning frameworks and Judge LLMs. |
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- **Open Access**: Designed to support researchers, educators, and developers in the field of quantum computing. |
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- **Scalable Infrastructure**: Metadata and structure optimized for efficient querying and usage. |
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### Example Domains |
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Some of the key domains covered in the dataset include: |
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- Synthetic Hamiltonians: Energy computations and time evolution. |
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- Quantum Spin Chains: Ising, Heisenberg, and advanced integrable models. |
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- Yang-Baxter Solvability: Solving for quantum integrable models. |
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- Trotter-Suzuki Decompositions: Efficient simulation of Hamiltonian dynamics. |
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- Quantum Phase Estimation: Foundational in quantum algorithms. |
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- Variational Quantum Eigensolvers (VQE): Optimization for quantum chemistry. |
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- Randomized Circuit Optimization: Enhancing algorithm robustness in noisy conditions. |
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- Quantum Thermodynamics: Gibbs state preparation and entropy calculations. |
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### Contributions |
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This dataset represents a collaborative effort to advance quantum computing research through the use of large-scale LLMs. It offers: |
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1. A scalable and comprehensive dataset for fine-tuning LLMs. |
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2. Rigorous methodologies for generating and validating quantum problem-solving tasks. |
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3. Open-access resources to foster collaboration and innovation in the quantum computing community. |
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Cite: |
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@dataset{quantumllm_instruct, |
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title={QuantumLLMInstruct: A 500k LLM Instruction-Tuning Dataset with Problem-Solution Pairs for Quantum Computing}, |
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author={Shlomo Kashani}, |
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year={2025}, |
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url={https://huggingface.co/datasets/QuantumLLMInstruct} |
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} |
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