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Quantum Graph Neural Network (QGNN) – Task 5

This repository implements a Quantum Graph Neural Network (QGNN) to leverage graph-structured data in a quantum computing context. The circuit encodes graph features into qubits and applies entangling gates to capture relationships between nodes.

Structure

  • qgnn_circuit.py — constructs and visualizes the QGNN quantum circuit
  • Task5_Report.md — detailed description of the approach, circuit design, and results

Description

A QGNN circuit takes advantage of graph representations by:

  1. Encoding node features into qubit rotations (Ry, Rz).
  2. Applying controlled gates (e.g., CNOT) along edges to capture graph connectivity.
  3. Measuring qubits to extract graph-informed quantum features.

The circuit can be expanded with more qubits or layers to represent larger graphs.

Requirements

  • Python 3.10+
  • Cirq
  • TensorFlow Quantum (optional for hybrid experiments)
  • NumPy

Usage

Run:

python qgnn_circuit.py
to build and visualize the QGNN circuit.