1. Deep quantum neural networks equipped with backpropagation on a superconducting processor
- Author
-
Pan, Xiaoxuan, Lu, Zhide, Wang, Weiting, Hua, Ziyue, Xu, Yifang, Li, Weikang, Cai, Weizhou, Li, Xuegang, Wang, Haiyan, Song, Yi-Pu, Zou, Chang-Ling, Deng, Dong-Ling, and Sun, Luyan
- Subjects
Quantum Physics - 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 the first experimental demonstration of training deep quantum neural networks via the backpropagation algorithm with a six-qubit programmable superconducting processor. 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 explicitly showcase the advantages of deep quantum neural networks, including quantum analogue of the backpropagation algorithm and less stringent coherence-time requirement for their constituting physical qubits, thus providing a valuable guide for quantum machine learning applications with both near-term and future quantum devices., Comment: 7 pages (main text) + 11 pages (Supplementary Information), 10 figures
- Published
- 2022
- Full Text
- View/download PDF