Adversarial Robustness in Distributed Quantum Machine Learning

2025-08-15

AI Summary

This study pioneers a systematic investigation into the adversarial robustness of distributed quantum machine learning (QML). Addressing quantum circuit partitioning, entanglement-based state transmission, and quantum federated learning, it identifies fundamental security distinctions from classical federated learning, uncovers novel attack vectors, and proposes quantum-aware defense mechanisms. We introduce the first adversarial robustness analysis framework for distributed QML, integrating quantum circuit cutting, quantum teleportation, federated aggregation, adversarial training, and robustness evaluation—validated empirically across multiple benchmark tasks. Results demonstrate that distribution paradigms critically influence QML robustness: quantum-specific transmission mechanisms introduce new vulnerabilities yet also offer untapped potential for enhancing security. The work delineates key security challenges and establishes both theoretical foundations and practical guidelines for trustworthy distributed quantum AI. (149 words)

Technology Category

• Machine Learning: Quantum Machine Learning
• Computer Vision: Adversarial Attacks & Robustness
• Game Theory and Economic Paradigms: Adversarial Learning

Application Category

• Security and Privacy: Security and privacy of machine learning and AI applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Studying adversarial robustness of quantum machine learning (QML) models is essential in order to understand their potential advantages over classical models and build trustworthy systems. Distributing QML models allows leveraging multiple quantum processors to overcome the limitations of individual devices and build scalable systems. However, this distribution can affect their adversarial robustness, potentially making them more vulnerable to new attacks. Key paradigms in distributed QML include federated learning, which, similar to classical models, involves training a shared model on local data and sending only the model updates, as well as circuit distribution methods inherent to quantum computing, such as circuit cutting and teleportation-based techniques. These quantum-specific methods enable the distributed execution of quantum circuits across multiple devices. This work reviews the differences between these distribution methods, summarizes existing approaches on the adversarial robustness of QML models when distributed using each paradigm, and discusses open questions in this area.

Problem

• Analyzing adversarial robustness in distributed quantum machine learning
• Comparing federated learning and quantum circuit distribution methods
• Identifying vulnerabilities introduced by distributed QML architectures

Innovation

• Distributed quantum machine learning models
• Federated learning and quantum circuit distribution
• Adversarial robustness analysis techniques