Quantum negative sampling strategy for knowledge graph embedding with variational circuit

TL;DR

This paper proposes a quantum negative sampling strategy using superposition in a hybrid quantum-classical model for knowledge graph embedding, aiming to enhance training efficiency and performance.

Contribution

It introduces a novel quantum negative sampling method leveraging superposition, advancing quantum approaches in knowledge graph embedding.

Findings

Quantum negative sampling improves training efficiency.

Model achieves competitive performance on knowledge graph datasets.

Demonstrates potential quantum advantage in negative sampling strategies.

Abstract

Knowledge graph is a collection of facts, known as triples(head, relation, tail), which are represented in form of a network, where nodes are entities and edges are relations among the respective head and tail entities. Embedding of knowledge graph for facilitating downstream tasks such as knowledge graph completion, link prediction, recommendation, has been a major area of research recently in classical machine learning. Because the size of knowledge graphs are becoming larger, one of the natural choices is to exploit quantum computing for knowledge graph embedding. Recently, a hybrid quantum classical model for knowledge graph embedding has been studied in which a variational quantum circuit is trained. One of the important aspects in knowledge graph embedding is the sampling of negative triples, which plays a crucial role in efficient training of the model. In classical machine learning various negative sampling strategies have been studied. In quantum knowledge graph embedding model, although we can use these strategies in principle, it is natural to ask if we can exploit quantum advantage in negative sampling. In this article we study such a negative sampling strategy, which exploits quantum superposition, and evaluate the model's performance with a knowledge graph database.