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arXiv · 2203.09080

Experimental Quantum End-to-End Learning on a Superconducting Processor

Xiaoxuan Pan·Xi Cao·Weiting Wang·Ziyue Hua·Weizhou Cai·Xuegang Li·Haiyan Wang·Jiaqi Hu·Yipu Song·Dong-Ling Deng·Chang-Ling Zou·Re-Bing Wu·Luyan Sun
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Abstract

Machine learning can be substantially powered by a quantum computer owing to its huge Hilbert space and inherent quantum parallelism. In the pursuit of quantum advantages for machine learning with noisy intermediate-scale quantum devices, it was proposed that the learning model can be designed in an end-to-end fashion, i.e., the quantum ansatz is parameterized by directly manipulable control pulses without circuit design and compilation. Such gate-free models are hardware friendly and can fully exploit limited quantum resources. Here, we report the first experimental realization of quantum end-to-end machine learning on a superconducting processor. The trained model can achieve 98% recognition accuracy for two handwritten digits (via two qubits) and 89% for four digits (via three qubits) in the MNIST (Mixed National Institute of Standards and Technology) database. The experimental results exhibit the great potential of quantum end-to-end learning for resolving complex real-world tasks when more qubits are available.

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Xiaoxuan Pan, Xi Cao, Weiting Wang, Ziyue Hua, Weizhou Cai, Xuegang Li, Haiyan Wang, Jiaqi Hu, Yipu Song, Dong-Ling Deng, Chang-Ling Zou, Re-Bing Wu, Luyan Sun. 2022-03-17. Experimental Quantum End-to-End Learning on a Superconducting Processor. https://arxiv.org/abs/2203.09080

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