Experimental demonstration of quantum continual learning with superconducting qubits

Zhang, Chuanyu; Lu, Zhide; Zhao, Liangtian; Xu, Shibo; Li, Weikang; Wang, Ke; Chen, Jiachen; Wu, Yaozu; Jin, Feitong; Zhu, Xuhao; Gao, Yu; Tan, Ziqi; Cui, Zhengyi; Zhang, Aosai; Wang, Ning; Zou, Yiren; Li, Tingting; Shen, Fanhao; Zhong, Jiarun; Bao, Zehang; Zhu, Zitian; Song, Zixuan; Deng, Jinfeng; Dong, Hang; Zhang, Pengfei; Jiang, Wenjie; Sun, Zheng-Zhi; Shen, Pei-Xin; Li, Hekang; Guo, Qiujiang; Wang, Zhen; Hao, Jie; Wang, Hui; Deng, Dong‐Ling; Song, Chao

doi:10.1038/s41534-025-01174-y

preprintnpj Quantum InformationJan 6, 2026GOLD OA

Experimental demonstration of quantum continual learning with superconducting qubits

CZChuanyu Zhang ZLZhide Lu LZLiangtian Zhao SXShibo Xu WLWeikang Li

Zhejiang University · ShangHai JiAi Genetics & IVF Institute · +7 more institutions

Indexed inarxivcrossrefdatacitedoaj

Abstract

Quantum computers may outperform classical computers on machine learning tasks. Yet, quantum learning systems may suffer from catastrophic forgetting, which is widely believed to be an obstacle to achieving continual learning. Here, we report an experimental demonstration of quantum continual learning on a superconducting processor. In particular, we sequentially train a quantum classifier with three tasks, two about identifying real-life images and one on classifying quantum states, and demonstrate its catastrophic forgetting. To overcome this dilemma, we exploit the elastic weight consolidation strategy and show that the quantum classifier can incrementally retain knowledge across three tasks with an average…

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Keywords

Superconductivity
Quantum
Computer science
Physics
Quantum mechanics

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