A Comparative Analysis of Adversarial Robustness for Quantum and Classical Machine Learning Models
Guardado en:
| Publicado en: | arXiv.org (Apr 24, 2024), p. n/a |
|---|---|
| Autor principal: | |
| Otros Autores: | , |
| Publicado: |
Cornell University Library, arXiv.org
|
| Materias: | |
| Acceso en línea: | Citation/Abstract Full text outside of ProQuest |
| Etiquetas: |
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3046998610 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 035 | |a 3046998610 | ||
| 045 | 0 | |b d20240424 | |
| 100 | 1 | |a Wendlinger, Maximilian | |
| 245 | 1 | |a A Comparative Analysis of Adversarial Robustness for Quantum and Classical Machine Learning Models | |
| 260 | |b Cornell University Library, arXiv.org |c Apr 24, 2024 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a Quantum machine learning (QML) continues to be an area of tremendous interest from research and industry. While QML models have been shown to be vulnerable to adversarial attacks much in the same manner as classical machine learning models, it is still largely unknown how to compare adversarial attacks on quantum versus classical models. In this paper, we show how to systematically investigate the similarities and differences in adversarial robustness of classical and quantum models using transfer attacks, perturbation patterns and Lipschitz bounds. More specifically, we focus on classification tasks on a handcrafted dataset that allows quantitative analysis for feature attribution. This enables us to get insight, both theoretically and experimentally, on the robustness of classification networks. We start by comparing typical QML model architectures such as amplitude and re-upload encoding circuits with variational parameters to a classical ConvNet architecture. Next, we introduce a classical approximation of QML circuits (originally obtained with Random Fourier Features sampling but adapted in this work to fit a trainable encoding) and evaluate this model, denoted Fourier network, in comparison to other architectures. Our findings show that this Fourier network can be seen as a "middle ground" on the quantum-classical boundary. While adversarial attacks successfully transfer across this boundary in both directions, we also show that regularization helps quantum networks to be more robust, which has direct impact on Lipschitz bounds and transfer attacks. | |
| 653 | |a Machine learning | ||
| 653 | |a Regularization | ||
| 653 | |a Classification | ||
| 653 | |a Circuits | ||
| 653 | |a Robustness | ||
| 653 | |a Coding | ||
| 700 | 1 | |a Kilian Tscharke | |
| 700 | 1 | |a Debus, Pascal | |
| 773 | 0 | |t arXiv.org |g (Apr 24, 2024), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3046998610/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/2404.16154 |