# The role of entanglement for enhancing the efficiency of quantum kernels towards classification

arXiv: https://arxiv.org/abs/2209.05142 · HTML: https://mlsanity.com/paper/2209.05142/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 87 references that are in the map.
- First posted on arXiv 12 Sep 2022.

## Builds on (most cited first, 12 of 12)

- [Supervised learning with quantum enhanced feature spaces](https://mlsanity.com/paper/1804.11326/) (2018)
- [Power of data in quantum machine learning](https://mlsanity.com/paper/2011.01938/) (2020)
- [Parameterized quantum circuits as machine learning models](https://mlsanity.com/paper/1906.07682/) (2019)
- [Quantum Machine Learning](https://mlsanity.com/paper/1611.09347/) (2016)
- [Supervised quantum machine learning models are kernel methods](https://mlsanity.com/paper/2101.11020/) (2021)
- [q-means: A quantum algorithm for unsupervised machine learning](https://mlsanity.com/paper/1812.03584/) (2018)
- [Quantum support vector machine for big data classification](https://mlsanity.com/paper/1307.0471/) (2013)
- [Importance of Kernel Bandwidth in Quantum Machine Learning](https://mlsanity.com/paper/2111.05451/) (2021)
- [Traffic flow optimization using a quantum annealer](https://mlsanity.com/paper/1708.01625/) (2017)
- [Quantum Unsupervised and Supervised Learning on Superconducting Processors](https://mlsanity.com/paper/1909.04226/) (2019)
- [Supervised Quantum Learning without Measurements](https://mlsanity.com/paper/1612.05535/) (2016)
- [Social Media Analysis for Product Safety using Text Mining and Sentiment Analysis](https://mlsanity.com/paper/1510.05301/) (2015)

## Built on by (most cited first, 10 of 10)

- [Quantum Kernel Methods under Scrutiny: A Benchmarking Study](https://mlsanity.com/paper/2409.04406/) (2024)
- [Machine Learning Applications of Quantum Computing: A Review](https://mlsanity.com/paper/2406.13262/) (2024)
- [SentiQNF: A Novel Approach to Sentiment Analysis Using Quantum Algorithms and Neuro-Fuzzy Systems](https://mlsanity.com/paper/2412.12731/) (2024)
- [Quantum Natural Language Processing: A Comprehensive Review of Models, Methods, and Applications](https://mlsanity.com/paper/2504.09909/) (2025)
- [Harnessing Quantum Support Vector Machines for Cross-Domain Classification of Quantum States](https://mlsanity.com/paper/2407.00774/) (2024)
- [Embedding-Aware Quantum-Classical SVMs for Scalable Quantum Machine Learning](https://mlsanity.com/paper/2508.00024/) (2025)
- [Machine Learning in the Quantum Age: Quantum vs. Classical Support Vector Machines](https://mlsanity.com/paper/2310.10910/) (2023)
- [Representation Learning with Parameterised Quantum Circuits for Advancing Speech Emotion Recognition](https://mlsanity.com/paper/2501.12050/) (2025)
- [Quantum neural networks facilitating quantum state classification](https://mlsanity.com/paper/2504.06622/) (2025)
- [Entanglement Detection with Quantum-inspired Kernels and SVMs](https://mlsanity.com/paper/2508.17909/) (2025)

## Abstract (arXiv)

Quantum kernels are considered as potential resources to illustrate benefits of quantum computing in machine learning. Considering the impact of hyperparameters on the performance of a classical machine learning model, it is imperative to identify promising hyperparameters using quantum kernel methods in order to achieve quantum advantages. In this work, we analyse and classify sentiments of textual data using a new quantum kernel based on linear and full entangled circuits as hyperparameters for controlling the correlation among words. We also find that the use of linear and full entanglement further controls the expressivity of the Quantum Support Vector Machine (QSVM). In addition, we also compare the efficiency of the proposed circuit with other quantum circuits and classical machine learning algorithms. Our results show that the proposed fully entangled circuit outperforms all other fully or linearly entangled circuits in addition to classical algorithms for most of the features. In fact, as the feature increases the efficiency of our proposed fully entangled model also increases significantly.