PaperHunterAgent - QuantumResearchChain

A sophisticated multi-agent system designed to hunt, analyze, and map quantum science research papers. The system consists of three specialized agents that work together to provide comprehensive research insights.

🤖 Agents Overview

1. PaperHunterAgent

Goal: Find the most relevant and NEW quantum-science papers every day.

Features: - Searches arXiv categories: quant-ph, hep-th, cond-mat, cs.QC - Queries Semantic Scholar API for citation analysis - Filters papers by keywords, language, and length - Returns JSON formatted results with relevance scores

2. SummarizerAgent

Mission: Create concise but information-dense summaries of quantum science papers.

Features: - PDF text extraction and processing (from URL or local file) - LaTeX equation identification - Technical term glossary generation - Structured Markdown output format - Critical assessment and contribution analysis

3. ConceptMapAgent

Objective: Transform summaries into machine-readable concept graphs.

Features: - Entity extraction from research summaries - Relationship mapping between concepts - Mermaid diagram generation - Quantum physics domain knowledge integration

🚀 Quick Start

Prerequisites

bash pip install -r requirements.txt cp .env.example .env # add your Semantic Scholar API key

Basic Usage

```python from src.paperhunteragent import PaperHunterAgent from src.summarizeragent import SummarizerAgent from src.conceptmap_agent import ConceptMapAgent

Define your research interests

keywords = ["quantum error correction", "surface code", "logical qubit"]

Initialize agents

hunter = PaperHunterAgent(user_keywords=keywords) summarizer = SummarizerAgent() mapper = ConceptMapAgent()

Hunt for papers

papersjson = hunter.huntpapers(maxpapers=10) papers = json.loads(papersjson)

Summarize a paper

if papers: summary = summarizer.createsummary(papers[0]) conceptmap = mapper.createconceptmap(summary) print(concept_map) ```

Run Demo

```bash python main.py

Run in Docker

docker build -t paperhunter . docker run --rm -it --env-file .env paperhunter ```

📋 Output Formats

PaperHunterAgent Output (JSON)

json [ { "title": "Quantum Error Correction with Surface Codes", "authors": ["Last, F.", "Another, A."], "arxiv_id": "2401.00001", "doi": "10.1000/example or null", "published": "2024-01-15", "url_pdf": "https://arxiv.org/pdf/2401.00001.pdf", "abstract": "Abstract text...", "relevance_score": 85 } ]

SummarizerAgent Output (Markdown)

```markdown

Paper Title

| Field | Value | |-------|-------| | Authors | Author1, Author2 | | Published | 2024-01-15 | | Primary Topic | Quantum Error Correction | | Key Equations | Eq. 1: H = ..., Eq. 2: |ψ⟩ = ... |

TL;DR (≤ 120 words)

Concise summary of the paper...

Main Contributions

• First contribution • Second contribution

Critical Assessment

Why it matters: Explanation... Potential weaknesses: Limitations...

Glossary

| Term | Definition | |------|------------| | Qubit | Quantum bit | ```

ConceptMapAgent Output (Mermaid)

mermaid graph TD quantum_error_correction[Quantum Error Correction] -->|uses| surface_code[Surface Code] surface_code -->|depends_on| syndrome_measurement[Syndrome Measurement] logical_qubit[Logical Qubit] -->|depends_on| physical_qubit[Physical Qubit]

🔧 Configuration

Search Parameters

• arXiv Categories: quant-ph, hep-th, cond-mat, cs.QC
• Time Windows: Last 24h (expandable to ±7 days if <3 papers found)
• Paper Limits: 10 papers maximum output
• Language Filter: English only
• Length Filter: ≥6 pages (estimated)

Filtering Rules

• Excludes duplicates by arXiv ID or DOI
• Must match at least one user-defined keyword
• Automatic relevance scoring (0-100)

🛠️ Advanced Usage

Custom Keywords

```python

Define domain-specific keywords

custom_keywords = [ "quantum machine learning", "variational quantum eigensolvers", "quantum approximate optimization", "quantum neural networks" ]

hunter = PaperHunterAgent(userkeywords=customkeywords) ```

Batch Processing

```python

Process multiple papers

papers = json.loads(hunter.huntpapers(maxpapers=10))

summaries = [] concept_maps = []

for paper in papers: summary = summarizer.createsummary(paper) if "⚠️ Unable to parse PDF" not in summary: summaries.append(summary) conceptmaps.append(mapper.createconceptmap(summary)) ```

📊 API Integrations

arXiv API

• Direct Python library integration
• Real-time paper discovery
• Metadata extraction

Semantic Scholar API

• Citation network analysis
• Cross-reference discovery
• Enhanced paper relationships

🔍 Logging and Monitoring

The system includes comprehensive logging: python import logging logging.basicConfig(level=logging.INFO)

Logs are saved to quantum_research.log and include: - Search progress and results - PDF processing status - Error handling and recovery - Performance metrics

🚦 Error Handling

Common Issues and Solutions

1. PDF Parsing Failures

   • Fallback from pdfplumber to PyPDF2
   • Graceful degradation with "⚠️ Unable to parse PDF" message

2. API Rate Limits

   • Built-in retry mechanisms
   • Respectful request spacing

3. Network Timeouts

   • 30-second PDF download timeout
   • Connection error recovery

📈 Performance Considerations

• Batch Size: Recommended max 10 papers per hunt
• PDF Processing: Can be slow for large documents
• Memory Usage: PDF content temporarily stored in memory
• Network: Requires stable internet for API calls

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Add tests for new functionality
4. Follow the existing code style
5. Submit a pull request

📑 Citation

If you use this project in your research, please cite it using the information provided in CITATION.cff.

📝 License

This project is open source. Please check the license file for details.

🔗 Related Resources

• arXiv API Documentation
• Semantic Scholar API
• Mermaid Diagram Syntax
• Quantum Computing Papers

📞 Support

For issues, questions, or contributions, please open an issue in the repository.

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Happy Quantum Research Hunting! 🔬⚛️