Quantum Adversarial Machine Learning and Defense Strategies: Challenges and Opportunities

TL;DR

This paper discusses the importance of developing quantum-secure neural networks by proposing three design principles and exploring quantum strategies to enhance security against adversarial attacks in the quantum computing era.

Contribution

It introduces three novel quantum-secure design principles and discusses strategies and open issues for protecting neural networks from quantum adversarial threats.

Findings

Proposed three quantum-secure design principles.

Identified quantum strategies like quantum data anonymization.

Highlighted open issues and future research directions.

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.