mlsanityData to 14 Oct 2026

Adversarial Robustness in Distributed Quantum Machine Learning

arXiv 2508.11848 ↗First posted 16 Aug 2025quant-ph cs.ET cs.LGcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 145 of its 347 references that are in the map. First posted on arXiv 16 Aug 2025.

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145 of 347 references.

References · 145

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Controlled Steering-Based State Preparation for Adversarial-Robust Quantum Machine Learningcited by 0
  2. 2026Entanglement geometry separates circuit cutting, classical hardness, and trainabilitycited by 0