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  4. Quantum neural networks under depolarization noise: exploring white-box attacks and defenses
 
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2024
Journal Article
Title

Quantum neural networks under depolarization noise: exploring white-box attacks and defenses

Abstract
Leveraging the unique properties of quantum mechanics, quantum machine learning (QML) promises computational breakthroughs and enriched perspectives where traditional systems reach their boundaries. However, similarly to classical machine learning, QML is not immune to adversarial attacks. Quantum adversarial machine learning has become instrumental in highlighting the weak points of QML models when faced with adversarial crafted feature vectors. Diving deep into this domain, our exploration shines a light on the interplay between depolarization noise and adversarial robustness. While previous results enhanced robustness from adversarial threats through depolarization noise, our findings paint a different picture. Interestingly, adding depolarization noise discontinued the effect of providing further robustness for a multi-class classification scenario. Consolidating our findings, we conducted experiments with a multi-class classifier adversarially trained on gate-based quantum simulators, further elucidating this unexpected behavior.
Author(s)
Winderl, David
Fraunhofer-Institut für Kognitive Systeme IKS  
Franco, Nicola  
Fraunhofer-Institut für Kognitive Systeme IKS  
Lorenz, Jeanette Miriam  orcid-logo
Fraunhofer-Institut für Kognitive Systeme IKS  
Journal
Quantum machine intelligence  
Project(s)
BayQC
Funder
Bayerisches Staatsministerium für Wirtschaft, Landesentwicklung und Energie  
Open Access
DOI
10.1007/s42484-024-00208-6
Language
English
Fraunhofer-Institut für Kognitive Systeme IKS  
Fraunhofer Group
Fraunhofer-Verbund IUK-Technologie  
Keyword(s)
  • quantum machine learning

  • QML

  • quantum computing

  • QC

  • adversarial robustness

  • adversarial attack

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