QGHNN: A quantum graph Hamiltonian neural network

TL;DR

This paper introduces QGHNN, a quantum graph Hamiltonian neural network that leverages quantum Hamiltonian learning to improve graph representation and learning on noisy quantum computers, outperforming existing methods.

Contribution

It proposes a novel quantum graph Hamiltonian neural network (QGHNN) with a quantum graph Hamiltonian learning method (QGHL) for enhanced graph learning on noisy quantum devices.

Findings

QGHNN achieves the lowest mean squared error of 0.004

QGHNN attains 99.8% maximum cosine similarity

QGHNN demonstrates high robustness and potential for quantum graph applications

Abstract

Representing and learning from graphs is essential for developing effective machine learning models tailored to non-Euclidean data. While Graph Neural Networks (GNNs) strive to address the challenges posed by complex, high-dimensional graph data, Quantum Neural Networks (QNNs) present a compelling alternative due to their potential for quantum parallelism. However, much of the current QNN research tends to overlook the vital connection between quantum state encoding and graph structures, which limits the full exploitation of quantum computational advantages. To address these challenges, this paper introduces a quantum graph Hamiltonian neural network (QGHNN) to enhance graph representation and learning on noisy intermediate-scale quantum computers. Concretely, a quantum graph Hamiltonian learning method (QGHL) is first created by mapping graphs to the Hamiltonian of the topological quantum system. Then, QGHNN based on QGHL is presented, which trains parameters by minimizing the loss function and uses the gradient descent method to learn the graph. Experiments on the PennyLane quantum platform reveal that QGHNN outperforms all assessment metrics, achieving the lowest mean squared error of \textbf{$0.004$} and the maximum cosine similarity of \textbf{$99.8\%$}, which shows that QGHNN not only excels in representing and learning graph information, but it also has high robustness ability. QGHNN can reduce the impact of quantum noise and has significant potential application in future research of quantum knowledge graphs and recommendation systems.