TeD-Q: a tensor network enhanced distributed hybrid quantum machine learning framework

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Bibliographic Details
Main Authors:	Chen, Yaocheng, Kuo, Chung-Yun, Du, Yuxuan, Tao, Dacheng, Wu, Xingyao
Format:	Preprint
Published:	2023
Subjects:	Quantum Physics Computational Physics
Online Access:	Acceder al recurso
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_version_	1866916511264800768
author	Chen, Yaocheng Kuo, Chung-Yun Du, Yuxuan Tao, Dacheng Wu, Xingyao
author_facet	Chen, Yaocheng Kuo, Chung-Yun Du, Yuxuan Tao, Dacheng Wu, Xingyao
contents	TeD-Q is an open-source software framework for quantum machine learning, variational quantum algorithm (VQA), and simulation of quantum computing. It seamlessly integrates classical machine learning libraries with quantum simulators, giving users the ability to leverage the power of classical machine learning while training quantum machine learning models. TeD-Q supports auto-differentiation that provides backpropagation, parameters shift, and finite difference methods to obtain gradients. With tensor contraction, simulation of quantum circuits with large number of qubits is possible. TeD-Q also provides a graphical mode in which the quantum circuit and the training progress can be visualized in real-time.
format	Preprint
id	arxiv_https___arxiv_org_abs_2301_05451
institution	arXiv
publishDate	2023
record_format	arxiv
spellingShingle	TeD-Q: a tensor network enhanced distributed hybrid quantum machine learning framework Chen, Yaocheng Kuo, Chung-Yun Du, Yuxuan Tao, Dacheng Wu, Xingyao Quantum Physics Computational Physics TeD-Q is an open-source software framework for quantum machine learning, variational quantum algorithm (VQA), and simulation of quantum computing. It seamlessly integrates classical machine learning libraries with quantum simulators, giving users the ability to leverage the power of classical machine learning while training quantum machine learning models. TeD-Q supports auto-differentiation that provides backpropagation, parameters shift, and finite difference methods to obtain gradients. With tensor contraction, simulation of quantum circuits with large number of qubits is possible. TeD-Q also provides a graphical mode in which the quantum circuit and the training progress can be visualized in real-time.
title	TeD-Q: a tensor network enhanced distributed hybrid quantum machine learning framework
topic	Quantum Physics Computational Physics
url	https://arxiv.org/abs/2301.05451

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