# Machine Learning in the Quantum Age: Quantum vs. Classical Support Vector Machines

arXiv: https://arxiv.org/abs/2310.10910 · HTML: https://mlsanity.com/paper/2310.10910/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 28 references that are in the map.
- First posted on arXiv 17 Oct 2023.

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

- [Supervised learning with quantum enhanced feature spaces](https://mlsanity.com/paper/1804.11326/) (2018)
- [Challenges and Opportunities in Quantum Machine Learning](https://mlsanity.com/paper/2303.09491/) (2023)
- [Quantum machine learning: a classical perspective](https://mlsanity.com/paper/1707.08561/) (2017)
- [Systematic Literature Review: Quantum Machine Learning and its applications](https://mlsanity.com/paper/2201.04093/) (2022)
- [The role of entanglement for enhancing the efficiency of quantum kernels towards classification](https://mlsanity.com/paper/2209.05142/) (2022)
- [Radial basis function kernel optimization for Support Vector Machine classifiers](https://mlsanity.com/paper/2007.08233/) (2020)
- [How to tune the RBF SVM hyperparameters?: An empirical evaluation of 18 search algorithms](https://mlsanity.com/paper/2008.11655/) (2020)

## Abstract (arXiv)

This work endeavors to juxtapose the efficacy of machine learning algorithms within classical and quantum computational paradigms. Particularly, by emphasizing on Support Vector Machines (SVM), we scrutinize the classification prowess of classical SVM and Quantum Support Vector Machines (QSVM) operational on quantum hardware over the Iris dataset. The methodology embraced encapsulates an extensive array of experiments orchestrated through the Qiskit library, alongside hyperparameter optimization. The findings unveil that in particular scenarios, QSVMs extend a level of accuracy that can vie with classical SVMs, albeit the execution times are presently protracted. Moreover, we underscore that augmenting quantum computational capacity and the magnitude of parallelism can markedly ameliorate the performance of quantum machine learning algorithms. This inquiry furnishes invaluable insights regarding the extant scenario and future potentiality of machine learning applications in the quantum epoch. Colab: https://t.ly/QKuz0