Quantum Kernels: Benchmark and Comparison with Classical ML classifiers

Thumbnail Image

Files

Lal_Ashutosh_2025.pdf (1.88 MB)

URL

Journal Title

Journal ISSN

Volume Title

Perustieteiden korkeakoulu | Bachelor's thesis
Unless otherwise stated, all rights belong to the author. You may download, display and print this publication for Your own personal use. Commercial use is prohibited.

Authors

Date

2025-09-19

Department

Major/Subject

Quantum Technology

Mcode

SCI3103

Degree programme

Aalto Bachelor’s Programme in Science and Technology

Language

en

Pages

25

Series

Abstract

Machine Learning has been remarkably successful and is transforming the way real life problems are solved. Quantum Computing is a developing field that utilizes different quantum phenomena for computational speedup. Quantum Machine Learning (QML) focuses on combining these two fields in a meaningful manner. Quantum kernels are among the state of the art approaches to QML. This thesis reproduces the existing work on quantum kernels and then expands on its real life application. These applications are addressed through two types of studies, comparison of quantum kernel based models with classical ML models and analysis of their performance on the present day error prone quantum hardware using noise simulation. Both of these studies are implemented using Qiskit in Python. The comparison results show that the quantum kernels can achieve better results than their classical counterpart. Noise simulations show that the model is resilient to lower levels of noise. However, further investigation is needed to explain the overall noise trends.

Description

Supervisor

Raasakka, Matti

Thesis advisor

Lado, Jose

Keywords

quantum kernels, quantum machine learning

Other note

Citation

Permanent link to this item

https://urn.fi/URN:NBN:fi:aalto-202510218399

Collections

[kand] Perustieteiden korkeakoulu / SCI