2024
DOI: 10.1088/1402-4896/ad2146
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Quantum neural network with privacy protection of input data and training parameters

Cheng Fang,
Yan Chang

Abstract: It takes a lot of computational resources to train a machine learning model. So does quantum machine learning. In the NISQ (Noisy Intermediate Scale Quantum) era, there is a need for users who cannot afford quantum computers to utilize quantum servers to complete quantum machine learning with protection privacy of data and model parameters. In this paper, novel homomorphic encryption methods and circuit design with hidden parameter for … Show more

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