Matches in SemOpenAlex for { <https://semopenalex.org/work/W4287980717> ?p ?o ?g. }
Showing items 1 to 68 of
68
with 100 items per page.
- W4287980717 endingPage "814" @default.
- W4287980717 startingPage "805" @default.
- W4287980717 abstract "Quantum adversarial machine learning lies at the intersection of quantum computing and adversarial machine learning. As the attainment of quantum supremacy demonstrates, quantum computers have already outpaced classical computers in certain domains (Arute et al. in Nature 574:505–510, 2019 [3]). The study of quantum computation is becoming increasingly relevant in today’s world. A field in which quantum computing may be applied is adversarial machine learning. A step toward better understanding quantum computing applied to adversarial machine learning has been taken recently by Lu et al. (Phys Rev Res 2:1–18, 2020 [13]), who have shown that gradient-based adversarial attacks can be transferred from classical to quantum neural networks. Inspired by Lu et al. (Phys Rev Res 2:1–18, 2020 [13]), we investigate the existence of the transferability of adversarial examples between different neural networks and the implications of that transferability. We find that, when the fast gradient sign attacks, as described by Goodfellow et al. (Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 [9]), is applied to a quantum neural network, the adversarially perturbed images produced with that method have transferability between quantum neural networks and from quantum to classical neural networks. In other words, adversarial images produced to deceive a quantum neural network can also deceive other quantum and classical neural networks. The results demonstrate that there exists transferability of adversarial examples in quantum machine learning. This transferability suggests a similarity in the decision boundaries of the different models, which may be an important subject of future study in quantum machine learning theory." @default.
- W4287980717 created "2022-07-26" @default.
- W4287980717 creator A5019172512 @default.
- W4287980717 creator A5023700993 @default.
- W4287980717 creator A5079072497 @default.
- W4287980717 date "2022-07-27" @default.
- W4287980717 modified "2023-09-25" @default.
- W4287980717 title "Transferability of Quantum Adversarial Machine Learning" @default.
- W4287980717 cites W1597479987 @default.
- W4287980717 cites W2095577883 @default.
- W4287980717 cites W2799194071 @default.
- W4287980717 cites W2943857514 @default.
- W4287980717 cites W3101479050 @default.
- W4287980717 cites W3102320711 @default.
- W4287980717 cites W3104599990 @default.
- W4287980717 doi "https://doi.org/10.1007/978-981-19-1610-6_71" @default.
- W4287980717 hasPublicationYear "2022" @default.
- W4287980717 type Work @default.
- W4287980717 citedByCount "1" @default.
- W4287980717 countsByYear W42879807172022 @default.
- W4287980717 crossrefType "book-chapter" @default.
- W4287980717 hasAuthorship W4287980717A5019172512 @default.
- W4287980717 hasAuthorship W4287980717A5023700993 @default.
- W4287980717 hasAuthorship W4287980717A5079072497 @default.
- W4287980717 hasConcept C119857082 @default.
- W4287980717 hasConcept C121332964 @default.
- W4287980717 hasConcept C140331021 @default.
- W4287980717 hasConcept C154945302 @default.
- W4287980717 hasConcept C2779094486 @default.
- W4287980717 hasConcept C37736160 @default.
- W4287980717 hasConcept C41008148 @default.
- W4287980717 hasConcept C50644808 @default.
- W4287980717 hasConcept C58053490 @default.
- W4287980717 hasConcept C61272859 @default.
- W4287980717 hasConcept C62520636 @default.
- W4287980717 hasConcept C80444323 @default.
- W4287980717 hasConcept C84114770 @default.
- W4287980717 hasConceptScore W4287980717C119857082 @default.
- W4287980717 hasConceptScore W4287980717C121332964 @default.
- W4287980717 hasConceptScore W4287980717C140331021 @default.
- W4287980717 hasConceptScore W4287980717C154945302 @default.
- W4287980717 hasConceptScore W4287980717C2779094486 @default.
- W4287980717 hasConceptScore W4287980717C37736160 @default.
- W4287980717 hasConceptScore W4287980717C41008148 @default.
- W4287980717 hasConceptScore W4287980717C50644808 @default.
- W4287980717 hasConceptScore W4287980717C58053490 @default.
- W4287980717 hasConceptScore W4287980717C61272859 @default.
- W4287980717 hasConceptScore W4287980717C62520636 @default.
- W4287980717 hasConceptScore W4287980717C80444323 @default.
- W4287980717 hasConceptScore W4287980717C84114770 @default.
- W4287980717 hasLocation W42879807171 @default.
- W4287980717 hasOpenAccess W4287980717 @default.
- W4287980717 hasPrimaryLocation W42879807171 @default.
- W4287980717 hasRelatedWork W2516574342 @default.
- W4287980717 hasRelatedWork W2559394418 @default.
- W4287980717 hasRelatedWork W2955078219 @default.
- W4287980717 hasRelatedWork W3045879969 @default.
- W4287980717 hasRelatedWork W3137915135 @default.
- W4287980717 hasRelatedWork W3189829096 @default.
- W4287980717 hasRelatedWork W3217374434 @default.
- W4287980717 hasRelatedWork W4287262419 @default.
- W4287980717 hasRelatedWork W4287980717 @default.
- W4287980717 hasRelatedWork W4321473541 @default.
- W4287980717 isParatext "false" @default.
- W4287980717 isRetracted "false" @default.
- W4287980717 workType "book-chapter" @default.