Quantum Graph Hash (QGH_256)

Using Classical Random Walks + Quantum Spectral Fingerprinting

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Concept Overview

The Quantum Graph Hash (QGH_256) is a novel quantum cryptographic hash function developed using: Classical Random Walks + Spectral Fingerprinting using Quantum Phase Estimation Algorithms

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Methodology

1 Classical Random Walks

• A message-induced 2D random walk is performed on a 4×4 grid.
• Each move affects the edge weights and node connectivity, encoding message entropy.
• The final output is a weighted directed graph, representing the structural signature of the message.

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2 Quantum Phase Estimation (QPE)

• The graph Laplacian (Hermitian) is exponentiated to form a unitary operator.
• Using Suzuki–Trotter decomposition, the exponential is approximated efficiently.
• QPE extracts the eigenvalue spectrum, which serves as the spectral fingerprint (hash vector).
• The spectral components are then normalized to produce a QGH_256 hash.

for more information, kindly refer to the presentation ppt attached

Content

This repository includes the following files:

1. main.py – The implementation of the QGH_256
2. QGH_256.ipynb – A Jupyter Notebook containing the main code, along with testing procedures and result analysis.
3. Presentation (QGH_256).pdf – A presentation providing an overview and conceptual explanation of the idea.
4. Video Explanation (QGH_256).mp4 – A video walkthrough of the concept and implementation.
   • Youtube link :- https://youtu.be/03S2Khxl8HE
5. LICENSE – The MIT License governing the usage and distribution of this project.

Installation

Requirements

Ensure you have Python 3.9+ installed.

Install Dependencies

You can install all the necessary Python packages using pip:

pip install qiskit qiskit-aer qiskit-algorithms numpy networkx matplotlib scipy pandas

If you use this work, please cite:

Mohana Priya Thinesh Kumar, Pranavishvar Hariprakash, Quantum Graph Hash (QGH_256), IIT(ISM) Dhanbad, 2025.