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.

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Structure

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

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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.

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Requirements

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

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Usage

Run:

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