دسته بندی:

محاسبات کوانتومی - Quantum-Computing

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سال انتشار:

2022

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عنوان انگلیسی مقاله:

Quantum Federated Learning With Decentralized Data

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ترجمه فارسی عنوان مقاله:

یادگیری فدرال کوانتومی با داده های غیرمتمرکز

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منبع:

ieee - ieee Journal of Selected Topics in Quantum Electronics;2022;28;4;10:1109/JSTQE:2022:3170150

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نویسنده:

Rui Huang; Xiaoqing Tan; Qingshan Xu

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چکیده انگلیسی:

Variational quantum algorithm (VQA) accesses the centralized data to train the model, and using distributed computing can significantly improve the training overhead; however, the data is privacy sensitive. In this paper, we propose communication-efficient learning of VQA from decentralized data, which is so-called quantumfederated learning(QFL).Motivated by the classical federated learning algorithm, we improve data privacy by aggregating updates from local computation to share model parameters. Here, aiming to find approximate optima in the parameter landscape, we develop an extension of the conventional VQA. Finally, we deploy onthe TensorFlowQuantum processor within variational quantumtensor networks classifiers, approximate quantum optimization for the Ising model, and variational quantum eigensolver for molecular hydrogen. Our algorithm demonstrates model accuracy from decentralized data, which have higher performance on near-term processors. Importantly, QFL may inspire new investigations in the field of secure quantum machine learning.
Index Terms: Quantum algorithm | quantum computing | quantum information | quantum machine learning.

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قیمت: رایگان

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توضیحات اضافی:

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