Quantum Adversarial Machine Learning and Defense Strategies: Challenges and Opportunities

TL;DR

Quantum-secure adversarial learning addresses how quantum advances reshape adversarial ML threats and defenses. It proposes three design principles—post-quantum cryptography, quantum-resistant architectures, and transparent deployment—and surveys defense strategies such as QDAI, QuGAN/VQGAN, QEC, and QE. An empirical study on quantum-classical hybrids shows vulnerabilities and evaluates defense trade-offs, underscoring the need for integrated, resource-aware quantum-secure solutions. The work provides a groundwork for building robust quantum-enabled neural systems and guides future multidisciplinary research in quantum adversarial machine learning.

Abstract

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.

Figures (9)

• Figure 1: Design context for the HNN model and processing without defense mechanism.
• Figure 2: Design context for the HNN model and processing with a defense mechanism.
• Figure 3: The quantum circuit used for HNN model.
• Figure 4: The MNIST dataset sample.
• Figure 5: The EMNIST "Digits" dataset sample.