A Differentially Private Quantum Neural Network for Probabilistic Optimal Power Flow

TL;DR

This paper introduces a differentially private quantum neural network for probabilistic optimal power flow, enhancing privacy and efficiency in renewable energy management with improved accuracy and reduced parameters.

Contribution

It presents a novel privacy-preserving QNN model with theoretical differential privacy guarantees and enhanced nonlinearity for probabilistic OPF.

Findings

Prevents privacy leakage effectively.

Reduces model parameters by 90% compared to classical methods.

Achieves higher accuracy and stability in probabilistic OPF.

Abstract

The stochastic nature of renewable energy and load demand requires efficient and accurate solutions for probabilistic optimal power flow (OPF). Quantum neural networks (QNNs), which combine quantum computing and machine learning, offer computational advantages in approximating OPF by effectively handling high-dimensional data. However, adversaries with access to non-private OPF solutions can potentially infer sensitive load demand patterns, raising significant privacy concerns. To address this issue, we propose a privacy-preserving QNN model for probabilistic OPF approximation. By incorporating Gaussian noise into the training process, the learning algorithm achieves ($\varepsilon, \delta$)-differential privacy with theoretical guarantees. Moreover, we develop a strongly entangled quantum state to enhance the nonlinearity expressiveness of the QNN. Experimental results demonstrate that the proposed method successfully prevents privacy leakage without compromising the statistical properties of probabilistic OPF. Moreover, compared to classical private neural networks, the QNN reduces the number of parameters by 90% while achieving significantly higher accuracy and greater stability.