QuantumReady

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How it works
Output
Algorithms

Inspiration

What it does

How we built it

Challenges we ran into

Accomplishments that we're proud of

What we learned

What's next for QuantumReady

Inspiration

The quantum computing threat is no longer science fiction. We were inspired by the "Harvest Now, Decrypt Later" attack — where adversaries collect encrypted data TODAY to decrypt it once quantum computers arrive by 2030. We realized developers have no simple tool to detect this risk in their own code — so we built one.

What it does

QuantumReady scans source code for quantum-vulnerable cryptographic algorithms (RSA, ECC, MD5, SHA-1, Diffie-Hellman, Weak TLS), predicts quantum risk using a Random Forest ML classifier, generates a 0-100 risk score, and provides exact NIST FIPS 203/204/205-approved replacement code. Results are downloadable as a PDF security report.

How we built it

Backend: Python + Flask
Scanner: Regex-based static analysis engine detecting 8 vulnerability types with exact line numbers
ML Model: Random Forest Classifier (scikit-learn) — 60 samples, 8 features, 5-fold cross-validation
Risk Engine: Rule-based scoring (CRITICAL −25pts, HIGH −15pts, Score = max(0, 100 − penalties))
Frontend: Jinja2 + Vanilla JS with animated score gauge and code diff viewer
Report: ReportLab PDF generator
Deployment: Vercel

Challenges we ran into

Matching ML feature vector to scanner output across 8 vulnerability types
Building a scoring system that is both meaningful and explainable to non-security developers
Making line-level findings display clearly without overwhelming the user

Accomplishments that we're proud of

Built a fully working prototype that scans real code and finds real vulnerabilities
Integrated ML prediction with static analysis in one seamless pipeline
Every fix aligned with official NIST FIPS 203, 204, and 205 standards
Live deployed at quantum-three-neon.vercel.app — free, zero setup

What we learned

Post-quantum cryptography standards (CRYSTALS-Kyber, Dilithium, SPHINCS+) in depth
How to train and validate a Random Forest classifier for security classification
The real urgency of quantum threat — $3.5 trillion in global data is at risk