The Impact of Feature Embedding Placement in the Ansatz of a Quantum Kernel in QSVMs

TL;DR

This paper investigates how the placement of feature embedding within quantum kernel ansatzes affects performance in QSVMs, revealing that existing architectures may not behave as expected and proposing a more efficient alternative.

Contribution

The study categorizes architectural patterns of quantum embedding kernels and introduces a new, simpler architecture that maintains performance with fewer gates.

Findings

Existing architectures do not behave as literature suggests.

A new architecture performs equally well with fewer gates.

Architectural choices significantly impact quantum kernel effectiveness.

Abstract

Designing a useful feature map for a quantum kernel is a critical task when attempting to achieve an advantage over classical machine learning models. The choice of circuit architecture, i.e. how feature-dependent gates should be interwoven with other gates is a relatively unexplored problem and becomes very important when using a model of quantum kernels called Quantum Embedding Kernels (QEK). We study and categorize various architectural patterns in QEKs and show that existing architectural styles do not behave as the literature supposes. We also produce a novel alternative architecture based on the old ones and show that it performs equally well while containing fewer gates than its older counterparts.