# Enhancing Quantum Support Vector Machines through Variational Kernel Training

arXiv: https://arxiv.org/abs/2305.06063 · HTML: https://mlsanity.com/paper/2305.06063/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 33 references that are in the map.
- First posted on arXiv 10 May 2023.

## Builds on (most cited first, 7 of 7)

- [Supervised learning with quantum enhanced feature spaces](https://mlsanity.com/paper/1804.11326/) (2018)
- [The effect of data encoding on the expressive power of variational quantum machine learning models](https://mlsanity.com/paper/2008.08605/) (2020)
- [A rigorous and robust quantum speed-up in supervised machine learning](https://mlsanity.com/paper/2010.02174/) (2020)
- [Supervised quantum machine learning models are kernel methods](https://mlsanity.com/paper/2101.11020/) (2021)
- [Practical application improvement to Quantum SVM: theory to practice](https://mlsanity.com/paper/2012.07725/) (2020)
- [Variational Quantum Approximate Support Vector Machine with Inference Transfer](https://mlsanity.com/paper/2206.14507/) (2022)
- [Support Vector Machines on Noisy Intermediate Scale Quantum Computers](https://mlsanity.com/paper/1909.11988/) (2019)

## Built on by (most cited first, 14 of 14)

- [QFNN-FFD: Quantum Federated Neural Network for Financial Fraud Detection](https://mlsanity.com/paper/2404.02595/) (2024)
- [Comparative Performance Analysis of Quantum Machine Learning Architectures for Credit Card Fraud Detection](https://mlsanity.com/paper/2412.19441/) (2024)
- [LEP-QNN: Loan Eligibility Prediction using Quantum Neural Networks](https://mlsanity.com/paper/2412.03158/) (2024)
- [MQFL-FHE: Multimodal Quantum Federated Learning Framework with Fully Homomorphic Encryption](https://mlsanity.com/paper/2412.01858/) (2024)
- [A Privacy-Preserving Federated Framework with Hybrid Quantum-Enhanced Learning for Financial Fraud Detection](https://mlsanity.com/paper/2507.22908/) (2025)
- [Quantum Support Vector Machine for Prostate Cancer Detection: A Performance Analysis](https://mlsanity.com/paper/2403.07856/) (2024)
- [Graph-Based Bayesian Optimization for Quantum Circuit Architecture Search with Uncertainty Calibrated Surrogates](https://mlsanity.com/paper/2512.09586/) (2025)
- [Flood Prediction Using Classical and Quantum Machine Learning Models](https://mlsanity.com/paper/2407.01001/) (2024)
- [RobQFL: Robust Quantum Federated Learning in Adversarial Environment](https://mlsanity.com/paper/2509.04914/) (2025)
- [Quantum vs. Classical Machine Learning: A Benchmark Study for Financial Prediction](https://mlsanity.com/paper/2601.03802/) (2026)
- [Quantum Kernels for Audio Deepfake Detection Using Spectrogram Patch Features](https://mlsanity.com/paper/2605.06035/) (2026)
- [Hybrid Quantum-Classical Neural Architecture Search](https://mlsanity.com/paper/2605.18345/) (2026)
- [Quantum-Powered Personalized Learning](https://mlsanity.com/paper/2408.15287/) (2024)
- [A Novel Double Pruning method for Imbalanced Data using Information Entropy and Roulette Wheel Selection for Breast Cancer Diagnosis](https://mlsanity.com/paper/2503.12239/) (2025)

## Abstract (arXiv)

Quantum machine learning (QML) has witnessed immense progress recently, with quantum support vector machines (QSVMs) emerging as a promising model. This paper focuses on the two existing QSVM methods: quantum kernel SVM (QK-SVM) and quantum variational SVM (QV-SVM). While both have yielded impressive results, we present a novel approach that synergizes the strengths of QK-SVM and QV-SVM to enhance accuracy. Our proposed model, quantum variational kernel SVM (QVK-SVM), leverages the quantum kernel and quantum variational algorithm. We conducted extensive experiments on the Iris dataset and observed that QVK-SVM outperforms both existing models in terms of accuracy, loss, and confusion matrix indicators. Our results demonstrate that QVK-SVM holds tremendous potential as a reliable and transformative tool for QML applications. Hence, we recommend its adoption in future QML research endeavors.