Fast variational knowledge graph embedding

• URL: http://arxiv.org/abs/2507.02472v1
• Date: Thu, 03 Jul 2025 09:30:23 GMT
• Title: Fast variational knowledge graph embedding
• Authors: Pulak Ranjan Giri, Mori Kurokawa, Kazuhiro Saito,
• Abstract summary: Quantum computer can help speedup the embedding process of the KGs.<n>We exploit additional quantum advantage by training multiple elements of KG in superpositions.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Embedding of a knowledge graph(KG) entities and relations in the form of vectors is an important aspect for the manipulation of the KG database for several downstream tasks, such as link prediction, knowledge graph completion, and recommendation. Because of the growing size of the knowledge graph databases, it has become a daunting task for the classical computer to train a model efficiently. Quantum computer can help speedup the embedding process of the KGs by encoding the entities into a variational quantum circuit of polynomial depth. Usually, the time complexity for such variational circuit-dependent quantum classical algorithms for each epoch is $\mathcal{O}(N \mbox{poly}(\log M))$, where $N$ is number of elements in the knowledge graph and $M$ is the number of features of each entities of the knowledge graph. In this article we exploit additional quantum advantage by training multiple elements of KG in superpositions, thereby reducing the computing time further for the knowledge graph embedding model.

Related papers

• Quantum negative sampling strategy for knowledge graph embedding with variational circuit [0.0]
  A hybrid quantum classical model for knowledge graph embedding has been studied in which a variational quantum circuit is trained.<n>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.
  arXiv  Detail & Related papers  (2025-02-25T08:46:27Z)
• Many-body quantum resources of graph states [0.0]
  Characterizing the non-classical correlations of a complex many-body system is an important part of quantum technologies. We consider four topologies, namely the star graph states with edges, Tur'an graphs, $r$-ary tree graphs, and square grid cluster states. We characterize many-body entanglement depth in graph states with up to $8$ qubits in $146$ classes non-equivalent under local transformations and graph isomorphisms.
  arXiv  Detail & Related papers  (2024-10-16T12:05:19Z)
• Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuits [63.733312560668274]
  Given a quantum circuit containing d tunable RZ gates and G-d Clifford gates, can a learner perform purely classical inference to efficiently predict its linear properties? We prove that the sample complexity scaling linearly in d is necessary and sufficient to achieve a small prediction error, while the corresponding computational complexity may scale exponentially in d. We devise a kernel-based learning model capable of trading off prediction error and computational complexity, transitioning from exponential to scaling in many practical settings.
  arXiv  Detail & Related papers  (2024-08-22T08:21:28Z)
• Graph Mixture of Experts: Learning on Large-Scale Graphs with Explicit Diversity Modeling [60.0185734837814]
  Graph neural networks (GNNs) have found extensive applications in learning from graph data. To bolster the generalization capacity of GNNs, it has become customary to augment training graph structures with techniques like graph augmentations. This study introduces the concept of Mixture-of-Experts (MoE) to GNNs, with the aim of augmenting their capacity to adapt to a diverse range of training graph structures.
  arXiv  Detail & Related papers  (2023-04-06T01:09:36Z)
• The Basis of Design Tools for Quantum Computing: Arrays, Decision Diagrams, Tensor Networks, and ZX-Calculus [55.58528469973086]
  Quantum computers promise to efficiently solve important problems classical computers never will. A fully automated quantum software stack needs to be developed. This work provides a look "under the hood" of today's tools and showcases how these means are utilized in them, e.g., for simulation, compilation, and verification of quantum circuits.
  arXiv  Detail & Related papers  (2023-01-10T19:00:00Z)
• MEKER: Memory Efficient Knowledge Embedding Representation for Link Prediction and Question Answering [65.62309538202771]
  Knowledge Graphs (KGs) are symbolically structured storages of facts. KG embedding contains concise data used in NLP tasks requiring implicit information about the real world. We propose a memory-efficient KG embedding model, which yields SOTA-comparable performance on link prediction tasks and KG-based Question Answering.
  arXiv  Detail & Related papers  (2022-04-22T10:47:03Z)
• From Quantum Graph Computing to Quantum Graph Learning: A Survey [86.8206129053725]
  We first elaborate the correlations between quantum mechanics and graph theory to show that quantum computers are able to generate useful solutions. For its practicability and wide-applicability, we give a brief review of typical graph learning techniques. We give a snapshot of quantum graph learning where expectations serve as a catalyst for subsequent research.
  arXiv  Detail & Related papers  (2022-02-19T02:56:47Z)
• Towards Quantum Graph Neural Networks: An Ego-Graph Learning Approach [47.19265172105025]
  We propose a novel hybrid quantum-classical algorithm for graph-structured data, which we refer to as the Ego-graph based Quantum Graph Neural Network (egoQGNN) egoQGNN implements the GNN theoretical framework using the tensor product and unity matrix representation, which greatly reduces the number of model parameters required. The architecture is based on a novel mapping from real-world data to Hilbert space.
  arXiv  Detail & Related papers  (2022-01-13T16:35:45Z)
• SMORE: Knowledge Graph Completion and Multi-hop Reasoning in Massive Knowledge Graphs [147.73127662757335]
  We present scalable Multi-hOp REasoning (SMORE), the first general framework for both single-hop and multi-hop reasoning in Knowledge Graphs (KGs) Using a single machine SMORE can perform multi-hop reasoning in Freebase KG (86M entities, 338M edges), which is 1,500x larger than previously considered KGs. SMORE increases throughput (i.e., training speed) over prior multi-hop KG frameworks by 2.2x with minimal GPU memory requirements.
  arXiv  Detail & Related papers  (2021-10-28T05:02:33Z)
• Quantum machine learning of graph-structured data [0.38581147665516596]
  We consider graph-structured quantum data and describe how to carry out its quantum machine learning via quantum neural networks. We explain how to systematically exploit this additional graph structure to improve quantum learning algorithms.
  arXiv  Detail & Related papers  (2021-03-19T14:39:19Z)
• Quantum Machine Learning Algorithm for Knowledge Graphs [35.149125599812706]
  Implicit knowledge can be inferred by modeling and reconstructing the tensor representations generated from knowledge graphs. This paper investigates how quantum resources can be capitalized to accelerate the modeling of knowledge graphs.
  arXiv  Detail & Related papers  (2020-01-04T13:26:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.