Related papers: A Numerical Gradient Inversion Attack in Variational Quantum Neural-Networks

A Numerical Gradient Inversion Attack in Variational Quantum Neural-Networks

URL: http://arxiv.org/abs/2504.12806v1
Date: Thu, 17 Apr 2025 10:12:38 GMT
Title: A Numerical Gradient Inversion Attack in Variational Quantum Neural-Networks
Authors: Georgios Papadopoulos, Shaltiel Eloul, Yash Satsangi, Jamie Heredge, Niraj Kumar, Chun-Fu Chen, Marco Pistoia,
Abstract summary: The loss landscape of Variational Quantum Neural Networks (VQNNs) is characterized by local minima that grow exponentially with increasing qubits.<n>We present a numerical scheme that successfully reconstructs input training, real-world, practical data from trainable VQNNs' gradients.
Score: 4.086403209504347
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: The loss landscape of Variational Quantum Neural Networks (VQNNs) is characterized by local minima that grow exponentially with increasing qubits. Because of this, it is more challenging to recover information from model gradients during training compared to classical Neural Networks (NNs). In this paper we present a numerical scheme that successfully reconstructs input training, real-world, practical data from trainable VQNNs' gradients. Our scheme is based on gradient inversion that works by combining gradients estimation with the finite difference method and adaptive low-pass filtering. The scheme is further optimized with Kalman filter to obtain efficient convergence. Our experiments show that our algorithm can invert even batch-trained data, given the VQNN model is sufficiently over-parameterized.

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