# Quantum Machine Learning Algorithm for Knowledge Graphs

arXiv: https://arxiv.org/abs/2001.01077 · HTML: https://mlsanity.com/paper/2001.01077/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jan 2020.

## Built on by (most cited first, 4 of 4)

- [Quantum Perceptron Revisited: Computational-Statistical Tradeoffs](https://mlsanity.com/paper/2106.02496/) (2021)
- [Quantum version of the k-NN classifier based on a quantum sorting algorithm](https://mlsanity.com/paper/2204.03761/) (2022)
- [A review of feature selection strategies utilizing graph data structures and knowledge graphs](https://mlsanity.com/paper/2406.14864/) (2024)
- [FIDDLE: Reinforcement Learning for Quantum Fidelity Enhancement](https://mlsanity.com/paper/2510.15833/) (2025)

## Abstract (arXiv)

Semantic knowledge graphs are large-scale triple-oriented databases for knowledge representation and reasoning. Implicit knowledge can be inferred by modeling and reconstructing the tensor representations generated from knowledge graphs. However, as the sizes of knowledge graphs continue to grow, classical modeling becomes increasingly computational resource intensive. This paper investigates how quantum resources can be capitalized to accelerate the modeling of knowledge graphs. In particular, we propose the first quantum machine learning algorithm for making inference on tensorized data, e.g., on knowledge graphs. Since most tensor problems are NP-hard, it is challenging to devise quantum algorithms to support that task. We simplify the problem by making a plausible assumption that the tensor representation of a knowledge graph can be approximated by its low-rank tensor singular value decomposition, which is verified by our experiments. The proposed sampling-based quantum algorithm achieves exponential speedup with a runtime that is polylogarithmic in the dimension of knowledge graph tensor.