Quantum Adversarial Machine Learning and Defense Strategies: Challenges and Opportunities
Not on the map. Why?
Abstract (arXiv)
As quantum computing continues to advance, the development of quantum-secure neural networks is crucial to prevent adversarial attacks. This paper proposes three quantum-secure design principles: (1) using post-quantum cryptography, (2) employing quantum-resistant neural network architectures, and (3) ensuring transparent and accountable development and deployment. These principles are supported by various quantum strategies, including quantum data anonymization, quantum-resistant neural networks, and quantum encryption. The paper also identifies open issues in quantum security, privacy, and trust, and recommends exploring adaptive adversarial attacks and auto adversarial attacks as future directions. The proposed design principles and recommendations provide guidance for developing quantum-secure neural networks, ensuring the integrity and reliability of machine learning models in the quantum era.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 14 of its 54 references that are in the map. First posted on arXiv 16 Dec 2024.
Builds on
14 of 54 references.
References · 14
- 2016Adversarial Machine Learning at Scalecited by 936
- 2019Square Attack: a query-efficient black-box adversarial attack via random searchcited by 685
- 2017Keeping the Bad Guys Out: Protecting and Vaccinating Deep Learning with JPEG Compressioncited by 87
- 2019Quantum Adversarial Machine Learningcited by 67
- 2020Adversarial Attack Vulnerability of Medical Image Analysis Systems: Unexplored Factorscited by 40
- 2018Adversarial Perturbations Against Real-Time Video Classification Systemscited by 32
- 2021Quantum machine learning with differential privacycited by 31
- 2022Quantum Machine Learning Framework for Virtual Screening in Drug Discovery: a Prospective Quantum Advantagecited by 18
- 2021Detecting Adversarial Examples by Input Transformations, Defense Perturbations, and Votingcited by 16
- 2018Quantum computing cryptography: Finding cryptographic Boolean functions with quantum annealing by a 2000 qubit D-wave quantum computercited by 2
- 2023Exploring the Vulnerabilities of Machine Learning and Quantum Machine Learning to Adversarial Attacks using a Malware Dataset: A Comparative Analysiscited by 2
- 2023TrojanNet: Detecting Trojans in Quantum Circuits using Machine Learningcited by 2
2 more
- 2023Quantum Solutions to the Privacy vs. Utility Tradeoffcited by 1
- 2024Post-Quantum Cryptography Neural Networkcited by 1
Built on by
Citing papers per year: 2026: 1.
1 paper.