Campus Lost'n Found

Where Lost Meets Found On Campus.

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Inspiration

Every semester, students lose countless personal items—ID cards, wallets, laptops, and even small things like headphones. Tracking them down is stressful, time-consuming, and often impossible without a proper system. I wanted to build a solution that brings together technology and community to make lost-and-found faster, safer, and smarter. I was also inspired by the idea of using cutting-edge technology—like quantum computing—in practical, everyday problems. While campus lost-and-found is traditionally a very “classical” issue, I experimented with quantum machine learning to help identify item similarity patterns and improve matching accuracy between lost and found reports.

What it does

Campus Lost’n Found is a web platform where students can:

  1. Report lost or found items with descriptions and photos.
  2. Use AI-powered (hybrid quantum-classical) matching to suggest potential item-owner matches.
  3. Get notified when their lost item has a potential match.
  4. Build trust by verifying claims through student email authentication.

How we built it

Frontend: Vue.js + Tailwind for a clean, responsive UI. Backend: Node.js with Express for APIs and MongoDB for storing reports and matches. Quantum Component: Used Qiskit to experiment with a Quantum k-Nearest Neighbors (QkNN) approach for item matching. Quantum embeddings were tested for semantic similarity of item descriptions, combined with a classical classifier for efficiency. Integration: The hybrid model runs classical filtering first (basic keyword and image similarity) and then leverages Qiskit models for refining the top matches.

Challenges we ran into

Quantum barrier: Setting up Qiskit and running circuits efficiently was tricky. Quantum simulations on a laptop are slow, and real quantum backends (IBM Quantum) have queue times. Hybrid integration: Combining classical ML with quantum embeddings required experimenting with multiple architectures before getting stable results. Scalability: Making sure the matching system was fast enough for real-time use while still incorporating quantum experiments was a balancing act. User adoption: Designing a UX simple enough for students who just want to post and find items, without overwhelming them with "tech complexity."

Accomplishments that we're proud of

1.Built a fully functional lost-and-found platform that can genuinely help students. 2.Successfully integrated a quantum-inspired approach into a real-world problem. 3.Designed a clean, intuitive interface that received positive feedback during early testing. 4.Bridged classical web tech and quantum computing, which is not a common mix in student-facing tools.

What we learned

1.How to use Qiskit for hybrid machine learning, including quantum-enhanced similarity searches. 2.The importance of UX simplicity when solving real student problems. 3.That quantum computing, while not yet production-ready for all tasks, can enhance niche parts of workflows. 4.Real-world problems (like finding lost items) often need both technical innovation and community trust-building.

What's next for Campus Lost'n Found

1.Improve the quantum-classical matching pipeline with more efficient quantum embeddings. 2.Add image-based quantum feature extraction (e.g., using PennyLane for hybrid image encoding). 3.Build a mobile app for faster posting and real-time notifications. 4.Partner with campus administration to integrate with student ID systems and security offices for verified claims.

Expand the idea beyond one campus into a network of universities that share lost-and-found platforms.

Built With

  • cloudinary-(for-image-upload/management)-other-tools:-github-for-version-control
  • express.js-database:-mongodb-cloud-services:-ibm-quantum-(qiskit-runtime)
  • figma-for-ui/ux-design
  • frontend:-vue.js
  • heroku/render-(backend-hosting)-quantum-tools:-qiskit-(quantum-knn-+-embeddings)
  • pennylane-(hybrid-integration-experiments)-apis:-ibm-quantum-api
  • tailwind-css-backend:-node.js
  • vercel/netlify-(frontend-hosting)
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