Can Quantum Federated Learning Withstand Circuit-Level Backdoors?
Quantum Federated Learning (QFL) inherits the core vulnerability of federated optimization to malicious clients, while also introducing an attack surface from variational circuit training and measurement-driven gradients. This work proposes a novel CircUit-Level backdoor Threat (CULT) model that formalizes four stealthy attacks by exploiting quantum-aware mechanisms, including Grover, Pauli, Bit-flip, and Sign-flip. By enabling malicious clients on both in-training and post-training surfaces, these attacks can critically undermine the learning process. We establish a rigorous theoretical foundation to demonstrate attack stealthiness under standard smoothness assumptions. Experiments on th...
| Authors | Aakar Mathur, Mohammed Ruknuddin, Ashish Gupta |
|---|---|
| Primary category | quant-ph |
| Published | 18 May 2026 |
| Last updated at source | 18 May 2026 |
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