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

• URL: http://arxiv.org/abs/2301.05451v1
• Date: Fri, 13 Jan 2023 09:35:05 GMT
• Title: TeD-Q: a tensor network enhanced distributed hybrid quantum machine learning framework
• Authors: Yaocheng Chen, Xingyao Wu, Chung-Yun Kuo, Yuxuan Du, and Dacheng Tao
• Abstract summary: TeD-Q is an open-source software framework for quantum machine learning. It seamlessly integrates classical machine learning libraries with quantum simulators. It provides a graphical mode in which the quantum circuit and the training progress can be visualized in real-time.
• Score: 59.07246314484875
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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