# Harnessing Inherent Noises for Privacy Preservation in Quantum Machine Learning

arXiv: https://arxiv.org/abs/2312.11126 · HTML: https://mlsanity.com/paper/2312.11126/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 15 references that are in the map.
- First posted on arXiv 18 Dec 2023.

## Builds on (most cited first, 5 of 5)

- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy](https://mlsanity.com/paper/2303.00654/) (2023)
- [Quantum machine learning with differential privacy](https://mlsanity.com/paper/2103.06232/) (2021)
- [Quantum computing for energy systems optimization: Challenges and opportunities](https://mlsanity.com/paper/2003.00254/) (2020)
- [An Adaptive and Fast Convergent Approach to Differentially Private Deep Learning](https://mlsanity.com/paper/1912.09150/) (2019)

## Built on by (most cited first, 3 of 3)

- [Differentially Private Federated Quantum Learning via Quantum Noise](https://mlsanity.com/paper/2508.20310/) (2025)
- [Quantum Support Vector Regression for Robust Anomaly Detection](https://mlsanity.com/paper/2505.01012/) (2025)
- [Characterizing Privacy Risks of Quantum Machine Learning with Emergent Quantum-Native Access](https://mlsanity.com/paper/2609.05702/) (2026)

## Abstract (arXiv)

Quantum computing revolutionizes the way of solving complex problems and handling vast datasets, which shows great potential to accelerate the machine learning process. However, data leakage in quantum machine learning (QML) may present privacy risks. Although differential privacy (DP), which protects privacy through the injection of artificial noise, is a well-established approach, its application in the QML domain remains under-explored. In this paper, we propose to harness inherent quantum noises to protect data privacy in QML. Especially, considering the Noisy Intermediate-Scale Quantum (NISQ) devices, we leverage the unavoidable shot noise and incoherent noise in quantum computing to preserve the privacy of QML models for binary classification. We mathematically analyze that the gradient of quantum circuit parameters in QML satisfies a Gaussian distribution, and derive the upper and lower bounds on its variance, which can potentially provide the DP guarantee. Through simulations, we show that a target privacy protection level can be achieved by running the quantum circuit a different number of times.