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Prospects of Privacy Advantage in Quantum Machine Learning
We establish conditions on the encoding map such as classical simulatability, overlap with DLA basis, and its Fourier frequency characteristics that enable such a privacy breach of VQC models.
Expressive variational quantum circuits provide inherent privacy in federated learning
However, standard neural network-based federated learning models have been shown to be susceptible to data leakage from the gradients shared with the server.
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