Deep quantum neural networks on a superconducting processor

Xiaoxuan Pan, Zhide Lu, Weiting Wang, Ziyue Hua, Yifang Xu, Weikang Li, Weizhou Cai, Xuegang Li, Haiyan Wang, Yi-Pu Song, Chang-Ling Zou, Dong-Ling Deng () and Luyan Sun ()
Additional contact information
Xiaoxuan Pan: Tsinghua University
Zhide Lu: Tsinghua University
Weiting Wang: Tsinghua University
Ziyue Hua: Tsinghua University
Yifang Xu: Tsinghua University
Weikang Li: Tsinghua University
Weizhou Cai: Tsinghua University
Xuegang Li: Tsinghua University
Haiyan Wang: Tsinghua University
Yi-Pu Song: Tsinghua University
Chang-Ling Zou: University of Science and Technology of China
Dong-Ling Deng: Tsinghua University
Luyan Sun: Tsinghua University

Nature Communications, 2023, vol. 14, issue 1, 1-7

Abstract: Abstract Deep learning and quantum computing have achieved dramatic progresses in recent years. The interplay between these two fast-growing fields gives rise to a new research frontier of quantum machine learning. In this work, we report an experimental demonstration of training deep quantum neural networks via the backpropagation algorithm with a six-qubit programmable superconducting processor. We experimentally perform the forward process of the backpropagation algorithm and classically simulate the backward process. In particular, we show that three-layer deep quantum neural networks can be trained efficiently to learn two-qubit quantum channels with a mean fidelity up to 96.0% and the ground state energy of molecular hydrogen with an accuracy up to 93.3% compared to the theoretical value. In addition, six-layer deep quantum neural networks can be trained in a similar fashion to achieve a mean fidelity up to 94.8% for learning single-qubit quantum channels. Our experimental results indicate that the number of coherent qubits required to maintain does not scale with the depth of the deep quantum neural network, thus providing a valuable guide for quantum machine learning applications with both near-term and future quantum devices.

Date: 2023
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DOI: 10.1038/s41467-023-39785-8

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