QuantumPath

Track, classify, and understand quantum computing progress.

Live Demo: https://quantum-path-pi.vercel.app/

QuantumPath is an open-source research intelligence platform designed to aggregate, organize, and analyze papers from arXiv across the quantum computing landscape. The platform automatically classifies papers by hardware platform and research theme, generates summaries, and assigns importance scores—helping researchers and students stay current with rapid developments in quantum computing.

Motivation

Quantum computing is advancing across multiple hardware platforms (superconducting qubits, trapped ions, neutral atoms, photonic systems, and more) and theoretical domains simultaneously. Students, researchers, and engineers struggle to keep up with the influx of new papers and progress across these fragmented routes.

QuantumPath solves this by:

Aggregating the latest quantum computing papers from arXiv daily
Organizing papers by platform and research theme
Summarizing key findings automatically
Scoring research importance based on transparent rules
Enabling discovery through a searchable, filterable interface

The result: a structured dashboard for tracking quantum computing progress.

Features

✨ Data Collection & Processing

Automated daily arXiv ingestion (configurable search terms)
Rule-based classification into 8 hardware platforms
Classification into 12 research themes
Basic extractive summarization with "why it matters" context
Importance scoring (1-5) with transparent reasoning

🎯 Research Discovery

Dashboard homepage with platform overview
Searchable paper list (title, abstract, authors, tags)
Filterable by platform and theme
Platform-specific route pages with paper timelines
Rule-based importance badges for high-impact papers

⚙️ Data Pipeline

One-command update pipeline: python scripts/update_papers.py
GitHub Actions daily automation (06:00 UTC)
Local JSON-based data storage for MVP
Manual and automated workflow triggers

Supported Platforms & Themes

Quantum Computing Platforms

Superconducting Qubits - Circuit-based processors with microwave control
Trapped Ions - High-control ion traps with laser manipulation
Neutral Atoms / Rydberg - Atom arrays with Rydberg interactions
Photonic Quantum Computing - Photon-based quantum information
Semiconductor Spin Qubits - Spin qubits in quantum dots
Topological Quantum Computing - Topological protection approaches
NV Centers / Defects - Diamond and silicon defect centers
General / Other - Uncategorized or emerging platforms

Research Themes

Quantum Error Correction
Fault-Tolerant Quantum Computing
Quantum Algorithms
Quantum Simulation
Quantum Machine Learning
Quantum Control
Quantum Compilation
Noise Mitigation
Benchmarking
Many-Body Physics
Quantum Communication
General Theory

Tech Stack

Layer	Technology
Frontend	Next.js 14 + TypeScript + Tailwind CSS
Data Ingestion	Python 3.11, arXiv API, urllib
Data Storage	Local JSON files (MVP)
Automation	GitHub Actions (daily schedule)
Hosting	Vercel/GitHub Pages ready

Architecture

   arXiv API
     ↓
 fetch_arxiv.py
     ↓
 classify_papers.py
     ↓
 summarize_papers_basic.py
     ↓
 score_importance.py
     ↓
 data/papers.json
     ↓
 Next.js Frontend (pages, search, filters)

Quick Start

Prerequisites

Node.js 18+
Python 3.11+
npm or npm.cmd (Windows)

Installation

# Install frontend dependencies
npm.cmd install

# Start development server
npm.cmd run dev

The application will be available at http://localhost:3000

Windows PowerShell Note: If npm is blocked, use npm.cmd instead.

Building for Production

npm.cmd run build
npm.cmd run start

Data Pipeline

Manual Pipeline Execution

Run the complete pipeline in order:

# Option 1: One-command pipeline (recommended)
python scripts/update_papers.py

# Option 2: Individual steps
python scripts/fetch_arxiv.py          # Fetch from arXiv
python scripts/classify_papers.py      # Classify by platform/theme
python scripts/summarize_papers_basic.py  # Generate summaries
python scripts/score_importance.py     # Score importance (1-5)

Windows Python Note: If python doesn't work, try py scripts/update_papers.py

Automated Updates

Paper data is updated automatically every day at 06:00 UTC via GitHub Actions (.github/workflows/update-papers.yml).

Manual Trigger

To manually run the update workflow:

Navigate to Actions tab on GitHub
Select Update Paper Data workflow
Click Run workflow → Confirm

Timezone Reference

06:00 UTC =

01:00 EST (Eastern)
22:00 PST (Pacific, previous day)
07:00 CET (Central European)

Project Structure

QuantumPath/
├── app/                          # Next.js App Router pages
│   ├── page.tsx                 # Dashboard homepage
│   ├── papers/page.tsx          # Papers list with search/filters
│   ├── routes/page.tsx          # Route overview
│   ├── routes/[slug]/page.tsx   # Route detail pages
│   └── api/                     # API endpoints
├── components/                   # React components
│   ├── NavBar.tsx
│   ├── PaperCard.tsx            # Paper display card
│   ├── PapersList.tsx           # Filtered paper list
│   ├── PaperFilters.tsx         # Search & filter controls
│   └── RouteCard.tsx
├── types/                        # TypeScript types
│   ├── paper.ts
│   └── route.ts
├── data/                         # Local JSON data store
│   ├── papers.json              # Papers (auto-generated)
│   └── routes.json              # Platform routes (manual)
├── scripts/                      # Python data pipeline
│   ├── fetch_arxiv.py           # Fetch papers from arXiv
│   ├── classify_papers.py       # Platform/theme classification
│   ├── summarize_papers_basic.py # Extractive summarization
│   ├── score_importance.py      # Rule-based importance scoring
│   └── update_papers.py         # Master pipeline script
├── docs/                         # Documentation
├── .github/workflows/            # GitHub Actions
│   └── update-papers.yml        # Daily update workflow
├── package.json                  # Frontend dependencies
├── tsconfig.json                 # TypeScript config
├── tailwind.config.ts            # Tailwind CSS config
└── README.md                     # This file

Features in Detail

🔍 Search & Discovery

Full-text search across titles, abstracts, authors, and tags
Filter by platform and research theme
Importance badges (1-5 score) with reasoning
Case-insensitive, trimmed query matching

📊 Dashboard

Overview of papers by platform
Most recent papers across all routes
Quick statistics

🛣️ Route Exploration

Platform overview pages
Route-specific paper timelines (grouped by month)
Top research themes for each route
Related paper history

📈 Importance Scoring

Transparent rule-based scoring (1-5):

+1 for QEC/FTQC themes
+1 for key technical terms (logical qubits, surface codes, fault-tolerance, etc.)
+1 for platform-scale experimental work
+1 for large-scale architecture mentions
Capped at 5, includes human-readable reasons

⚙️ Automation

Daily updates via GitHub Actions (06:00 UTC)
One-command pipeline: python scripts/update_papers.py
Manual triggers via GitHub Actions UI
No database required (JSON-based MVP)

Development

Frontend Commands

npm.cmd install     # Install dependencies
npm.cmd run dev     # Development server (http://localhost:3000)
npm.cmd run build   # Production build
npm.cmd run lint    # ESLint validation

Python Environment

The Python scripts use standard library only (no external dependencies needed):

json, pathlib, subprocess, urllib, xml

Adding Custom Search Terms

Edit scripts/fetch_arxiv.py to customize arXiv search:

SEARCH_TERMS = [
    "quantum computing",
    "your custom term here",
    # ...
]

Important: Academic Disclaimer ⚠️

Summaries, classifications, and importance scores are automatically generated using rule-based heuristics and extractive summarization. They are not human-validated and should be treated as approximations.

Always:

✅ Verify findings against original papers
✅ Cross-reference with multiple sources
✅ Check dates and citations in source material
✅ Consult domain experts for critical decisions

This tool is designed to accelerate discovery, not replace human review.

Future Roadmap

Short Term (v0.2)

LLM-based abstractive summarization (with opt-in API)
PDF extraction and full-text indexing
Citation graph visualization
Paper recommendations based on reading history

Medium Term (v0.3-v0.4)

Semantic Scholar / OpenAlex metadata integration
Vector search with embeddings
Personalized research alerts
Multi-language support (Chinese, Spanish)
Export to BibTeX/Zotero

Long Term (v1.0)

Supabase/PostgreSQL backend migration
Trend visualization and charting
Collaborative collections and lists
Research group workspaces
Publication-style paper reports

Contributing

Contributions welcome! Please:

Fork the repository
Create a feature branch (git checkout -b feature/amazing-feature)
Commit changes (git commit -m 'Add amazing feature')
Push to branch (git push origin feature/amazing-feature)
Open a Pull Request

Ideas for Contribution

Add support for additional paper sources (bioRxiv, PapersWithCode, etc.)
Improve classification rules
Enhance summarization quality
Add visualization components
Optimize search performance
Improve mobile responsiveness

Performance & Scalability

Current (MVP)

~100+ papers per search term
Local JSON storage (fast for <10k papers)
Response time: <100ms for filters/search
Build time: ~10s, First Load JS: ~94kB

Limitations

JSON storage scales to ~10-50k papers before optimization needed
No full-text index (searches entire abstracts in memory)
Single-user experience (no concurrency handling)

Migration Path

PostgreSQL + Supabase for >50k papers
Full-text search indexes
Caching layer (Redis)
Vector database for semantic search

License

MIT License - see LICENSE file for details

Contact & Attribution

Author: Andy W
Email: [your email]
GitHub: [your github]

Special Thanks:

arXiv for the open API and research papers
Next.js, TypeScript, and Tailwind CSS communities
Open-source quantum computing research community

Last Updated: 2026-06-15
Status: MVP - Active Development

Name		Name	Last commit message	Last commit date
Latest commit History 43 Commits
.github/workflows		.github/workflows
app		app
components		components
data		data
scripts		scripts
types		types
.eslintrc.json		.eslintrc.json
.gitignore		.gitignore
AGENTS.md		AGENTS.md
README.md		README.md
next-env.d.ts		next-env.d.ts
next.config.mjs		next.config.mjs
package-lock.json		package-lock.json
package.json		package.json
postcss.config.js		postcss.config.js
tailwind.config.ts		tailwind.config.ts
tsconfig.json		tsconfig.json

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