# Differentially Private Federated Quantum Learning via Quantum Noise

## TL;DR

This paper introduces a quantum noise-based differential privacy mechanism for federated quantum learning, leveraging inherent quantum noise to enhance privacy and robustness on NISQ devices, balancing security and accuracy.

## Contribution

It presents a novel quantum noise-based differential privacy approach tailored for NISQ devices in federated quantum learning, addressing privacy and adversarial robustness.

## Key findings

- Effective privacy-robustness trade-off demonstrated
- Quantum noise tuning achieves desired differential privacy levels
- Framework resists adversarial attacks on quantum models

## Abstract

Quantum federated learning (QFL) enables collaborative training of quantum machine learning (QML) models across distributed quantum devices without raw data exchange. However, QFL remains vulnerable to adversarial attacks, where shared QML model updates can be exploited to undermine information privacy. In the context of noisy intermediate-scale quantum (NISQ) devices, a key question arises: How can inherent quantum noise be leveraged to enforce differential privacy (DP) and protect model information during training and communication? This paper explores a novel DP mechanism that harnesses quantum noise to safeguard quantum models throughout the QFL process. By tuning noise variance through measurement shots and depolarizing channel strength, our approach achieves desired DP levels tailored to NISQ constraints. Simulations demonstrate the framework's effectiveness by examining the relationship between differential privacy budget and noise parameters, as well as the trade-off between security and training accuracy. Additionally, we demonstrate the framework's robustness against an adversarial attack designed to compromise model performance using adversarial examples, with evaluations based on critical metrics such as accuracy on adversarial examples, confidence scores for correct predictions, and attack success rates. The results reveal a tunable trade-off between privacy and robustness, providing an efficient solution for secure QFL on NISQ devices with significant potential for reliable quantum computing applications.