دسته بندی:
محاسبات کوانتومی - Quantum-Computing
سال انتشار:
2022
عنوان انگلیسی مقاله:
Quantum Federated Learning With Decentralized Data
ترجمه فارسی عنوان مقاله:
یادگیری فدرال کوانتومی با داده های غیرمتمرکز
منبع:
ieee - ieee Journal of Selected Topics in Quantum Electronics;2022;28;4;10:1109/JSTQE:2022:3170150
نویسنده:
Rui Huang; Xiaoqing Tan; Qingshan Xu
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.
قیمت: رایگان
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