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2012.11619
Cited By

Defence against adversarial attacks using classical and quantum-enhanced
Boltzmann machines

v1v2 (latest)

Defence against adversarial attacks using classical and quantum-enhanced Boltzmann machines

21 December 2020

Alejandro Pozas-Kerstjens

ArXiv (abs)PDF HTML

Papers citing "Defence against adversarial attacks using classical and quantum-enhanced Boltzmann machines"

5 / 5 papers shown

Predominant Aspects on Security for Quantum Machine Learning: Literature
Review

Predominant Aspects on Security for Quantum Machine Learning: Literature ReviewInternational Conference on Quantum Computing and Engineering (QCE), 2024

Alona Sakhnenko

290

20

0

15 Jan 2024

Towards quantum enhanced adversarial robustness in machine learning

Towards quantum enhanced adversarial robustness in machine learningNature Machine Intelligence (Nat. Mach. Intell.), 2023

Maxwell T. West

Lloyd C. L. Hollenberg

270

82

0

22 Jun 2023

Benchmarking Adversarially Robust Quantum Machine Learning at Scale

Benchmarking Adversarially Robust Quantum Machine Learning at ScalePhysical Review Research (Phys. Rev. Res.), 2022

Maxwell T. West

Lloyd C. L. Hollenberg

219

63

0

23 Nov 2022

Accelerating the training of single-layer binary neural networks using
the HHL quantum algorithm

Accelerating the training of single-layer binary neural networks using the HHL quantum algorithmICCD (ICCD), 2022

Martin Hoffnagle

Alejandro Pozas-Kerstjens

180

7

0

23 Oct 2022

Generating Natural Adversarial Examples

Generating Natural Adversarial Examples

848

652

0

31 Oct 2017

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