# Quantum Adversarial Machine Learning and Defense Strategies: Challenges and Opportunities

arXiv: https://arxiv.org/abs/2412.12373 · HTML: https://mlsanity.com/paper/2412.12373/

## Key facts

- Cited by 1 papers 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 (most cited first, 14 of 14)

- [Adversarial Machine Learning at Scale](https://mlsanity.com/paper/1611.01236/) (2016)
- [Square Attack: a query-efficient black-box adversarial attack via random search](https://mlsanity.com/paper/1912.00049/) (2019)
- [Keeping the Bad Guys Out: Protecting and Vaccinating Deep Learning with JPEG Compression](https://mlsanity.com/paper/1705.02900/) (2017)
- [Quantum Adversarial Machine Learning](https://mlsanity.com/paper/2001.00030/) (2019)
- [Adversarial Attack Vulnerability of Medical Image Analysis Systems: Unexplored Factors](https://mlsanity.com/paper/2006.06356/) (2020)
- [Adversarial Perturbations Against Real-Time Video Classification Systems](https://mlsanity.com/paper/1807.00458/) (2018)
- [Quantum machine learning with differential privacy](https://mlsanity.com/paper/2103.06232/) (2021)
- [Quantum Machine Learning Framework for Virtual Screening in Drug Discovery: a Prospective Quantum Advantage](https://mlsanity.com/paper/2204.04017/) (2022)
- [Detecting Adversarial Examples by Input Transformations, Defense Perturbations, and Voting](https://mlsanity.com/paper/2101.11466/) (2021)
- [Quantum computing cryptography: Finding cryptographic Boolean functions with quantum annealing by a 2000 qubit D-wave quantum computer](https://mlsanity.com/paper/1806.08706/) (2018)
- [Exploring the Vulnerabilities of Machine Learning and Quantum Machine Learning to Adversarial Attacks using a Malware Dataset: A Comparative Analysis](https://mlsanity.com/paper/2305.19593/) (2023)
- [TrojanNet: Detecting Trojans in Quantum Circuits using Machine Learning](https://mlsanity.com/paper/2306.16701/) (2023)
- [Quantum Solutions to the Privacy vs. Utility Tradeoff](https://mlsanity.com/paper/2307.03118/) (2023)
- [Post-Quantum Cryptography Neural Network](https://mlsanity.com/paper/2402.16002/) (2024)

## Built on by (most cited first, 1 of 1)

- [Quantum Adversarial Machine Learning: From Classical Adaptations to Quantum-Native Methods](https://mlsanity.com/paper/2605.18821/) (2026)

## 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.