Federated Quantum Machine Learning with Differential Privacy

TL;DR

This paper introduces a novel approach combining Quantum Federated Learning and Quantum Differential Privacy to enhance data security in quantum machine learning, demonstrating high accuracy and strong privacy on NISQ devices.

Contribution

It is the first to integrate QFL and QDP in a quantum setting, providing a comprehensive privacy-preserving framework for quantum AI applications.

Findings

Achieved over 98% test accuracy on Cats vs Dogs dataset.

Maintained epsilon values below 1.3, indicating strong privacy.

Proved the viability of federated differentially private training on NISQ devices.

Abstract

The preservation of privacy is a critical concern in the implementation of artificial intelligence on sensitive training data. There are several techniques to preserve data privacy but quantum computations are inherently more secure due to the no-cloning theorem, resulting in a most desirable computational platform on top of the potential quantum advantages. There have been prior works in protecting data privacy by Quantum Federated Learning (QFL) and Quantum Differential Privacy (QDP) studied independently. However, to the best of our knowledge, no prior work has addressed both QFL and QDP together yet. Here, we propose to combine these privacy-preserving methods and implement them on the quantum platform, so that we can achieve comprehensive protection against data leakage (QFL) and model inversion attacks (QDP). This implementation promises more efficient and secure artificial intelligence. In this paper, we present a successful implementation of these privacy-preservation methods by performing the binary classification of the Cats vs Dogs dataset. Using our quantum-classical machine learning model, we obtained a test accuracy of over 0.98, while maintaining epsilon values less than 1.3. We show that federated differentially private training is a viable privacy preservation method for quantum machine learning on Noisy Intermediate-Scale Quantum (NISQ) devices.