Hey there, Dev.to community! 👋 I’m thrilled to share my latest project: Quantum arXiv Topic Modeling Analysis. As a newbie in data science, this was my first big dive into NLP (Natural Language Processing) and topic modeling, and I’m so excited to show you what I found in the world of quantum machine learning. Let’s get into it—I’d love to hear your thoughts!

What’s This Project About? 🤔

I set out to explore trends in quantum machine learning by analyzing research papers from arXiv. Using NLP and topic modeling (specifically LDA), I dug into a huge dataset to uncover the hottest topics in this field from 2015 to 2025. It was a challenging but super rewarding journey!

Here’s the quick rundown:

  • Started with 50,000+ arXiv papers.
  • Filtered down to 2,000+ papers related to quantum topics.
  • Used NLP to clean the abstracts (think tokenization, lemmatization, and stopword removal).
  • Applied LDA topic modeling to identify 5 key topics.
  • Visualized the trends over time with a neat plot.

The Tech I Used 🛠️

This project was a great chance to get hands-on with some cool tools. Here’s what I used:

  • Python: My go-to language for this project.
  • Pandas: For wrangling the dataset.
  • NLTK: To handle the NLP part—like cleaning up the abstracts.
  • Gensim: For LDA topic modeling (it’s amazing for finding hidden patterns!).
  • Matplotlib/Seaborn: To create a visualization of the trends.

I broke the work into three scripts:

  1. data_processing.py: Filters the dataset for quantum papers.
  2. topic_modeling.py: Cleans the data and runs LDA.
  3. visualization.py: Plots the topic trends.

What I Found 📊

After running my pipeline, I discovered 5 major topics in quantum machine learning research. Here they are:

  • Topic 1: Quantum states and entanglement
  • Topic 2: Quantum algorithms and optimization
  • Topic 3: Machine learning applications in quantum systems
  • Topic 4: Quantum error correction
  • Topic 5: Quantum cryptography and security

The best part? Seeing how these topics evolved over time. For example, quantum cryptography has been picking up steam, especially in 2025. Check out the trend plot I made:

Topic Trends

What I Learned 🌟

This project taught me so much as a beginner in data science. Here are my biggest takeaways:

  • NLP is fun but tricky: Cleaning text data took some trial and error, but NLTK was a lifesaver.
  • Topic modeling rocks: LDA helped me find patterns I wouldn’t have seen otherwise.
  • Git can be a challenge: I hit some bumps with large files (thanks, GitHub 100 MB limit!), but I figured out how to fix them with .gitignore and git filter-branch.
  • Visuals make a difference: Plotting the trends really brought the data to life.

What’s Next? 🔮

I’m really happy with how this turned out, but I’m always looking to improve. Here are some ideas I’m thinking about:

  • Add more interactivity to the demo (like filtering topics by year).
  • Analyze an even bigger dataset or zoom in on a specific quantum ML area.
  • Try a different topic modeling method, like BERTopic.

What do you think? I’d love to hear your ideas or suggestions in the comments!

Let’s Connect! 👋

Thanks for checking out my quantum machine learning project! If you’re into data science, NLP, or topic modeling, let’s chat—I’d love to connect. You can find me on GitHub or check out my portfolio (replace this with your actual portfolio link if you have one).

If you found this project interesting, I’d really appreciate a star ⭐ on the GitHub repo—it’d mean a lot! And if you try running the code, let me know how it goes. 😊

Happy coding, everyone!

How You Can Try It Out 🔧

Want to play around with my project? I’ve made it easy to run! Everything’s on GitHub, and here’s how you can get started:

  1. Clone the repo:
bash
   git clone https://github.com/Raiyan708/Quantum-Arxiv-Topic-Modeling.git
   cd Quantum-Arxiv-Topic-Modeling