Flexible Genetic Algorithm for Quantum Support Vector Machines

TL;DR

This paper introduces GA-QSVM, a hybrid quantum-classical framework that uses genetic algorithms to automatically optimize feature maps, improving the adaptability and performance of quantum support vector machines across various datasets.

Contribution

It presents a novel hybrid framework employing genetic algorithms to optimize quantum feature maps, enabling adaptive circuit design for QSVMs.

Findings

GA-QSVM achieves accuracy comparable to classical SVMs and standard QSVMs.

The method demonstrates effective transfer learning across datasets.

Genetic algorithms enhance the flexibility and generalization of quantum circuit design.

Abstract

Quantum Support Vector Machines (QSVM) is one of the most promising frameworks in quantum machine learning, yet their performance depends on the design of the feature map. Conventional approaches rely on fixed quantum circuits, which often fail to generalize across datasets. To address this limitation, we propose GA-QSVM, a hybrid framework that employs Genetic Algorithms (GA) to automatically optimize feature maps. The proposed method introduces a configurable framework that flexibly defines the evolutionary parameters, enabling the construction of adaptive circuits. Experimental evaluation of datasets, including Digits, Fashion, Wine, and Breast Cancer, demonstrates that GA-QSVMs achieve a comparable accuracy compared to classical SVMs and standard QSVMs. Furthermore, transfer learning results indicate that GA-QSVM's circuits generalize effectively across datasets. These findings highlight the potential of evolutionary strategies to automate and enhance kernel design for future quantum machine learning applications.