Distributed machine learning based on quantum cloud with quantum homomorphic encryption

doi:10.1016/j.future.2025.108053

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Distributed machine learning based on quantum cloud with quantum homomorphic encryption

2025-08-05

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DOI：10.1016/j.future.2025.108053

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Abstract

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In the era of Noisy Intermediate-Scale Quantum (NISQ) technology, variational quantum algorithms have emerged as a prominent method to showcase quantum superiority. Quantum federated learning (QFL), employing these algorithms in a distributed computing setting, enhances training performance and protects user data privacy. However, this approach significantly increases the demand for quantum capabilities, which are often beyond the reach of average users. This paper introduces a novel approach to QFL that incorporates quantum homomorphic encryption to enhance data security and privacy during collaborative training. We present the Quantum Circuit Random Reconstruction Homomorphic Encryption Algorithm (QCRRA), designed to facilitate secure and efficient model training over quantum networks without compromising data privacy. The QCRRA allows participants with no quantum capabilities to engage in federated learning by encrypting their quantum circuits, thus obviating direct exposure of sensitive data to quantum servers. We analyze the performance of our proposed approach through binary classification tasks on both MNIST, CIFAR10 and ad-hoc datasets, demonstrating that QCRRA maintains the integrity and accuracy of the learning process while significantly reducing the risk of data leakage. This study not only underscores the viability of QFL under stringent privacy constraints but also sets a precedent for future research in secure, decentralized quantum machine learning frameworks.

Keywords:

quantum federated learning

quantum homomorphic encryption

variational quantum algorithms

secure quantum machine learning

quantum circuit random reconstruction