Quantum continual learning on a programmable superconducting processor
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866912850241388544 |
|---|---|
| author | 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, H. Deng, Dong-Ling Song, Chao |
| author_facet | 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, H. Deng, Dong-Ling Song, Chao |
| contents | Quantum computers may outperform classical computers on machine learning tasks. In recent years, a variety of quantum algorithms promising unparalleled potential to enhance, speed up, or innovate machine learning have been proposed. Yet, quantum learning systems, similar to their classical counterparts, may likewise suffer from the catastrophic forgetting problem, where training a model with new tasks would result in a dramatic performance drop for the previously learned ones. This problem is widely believed to be a crucial obstacle to achieving continual learning of multiple sequential tasks. Here, we report an experimental demonstration of quantum continual learning on a fully programmable superconducting processor. In particular, we sequentially train a quantum classifier with three tasks, two about identifying real-life images and the other on classifying quantum states, and demonstrate its catastrophic forgetting through experimentally observed rapid performance drops for prior tasks. To overcome this dilemma, we exploit the elastic weight consolidation strategy and show that the quantum classifier can incrementally learn and retain knowledge across the three distinct tasks, with an average prediction accuracy exceeding 92.3%. In addition, for sequential tasks involving quantum-engineered data, we demonstrate that the quantum classifier can achieve a better continual learning performance than a commonly used classical feedforward network with a comparable number of variational parameters. Our results establish a viable strategy for empowering quantum learning systems with desirable adaptability to multiple sequential tasks, marking an important primary experimental step towards the long-term goal of achieving quantum artificial general intelligence. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_09729 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Quantum continual learning on a programmable superconducting processor 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, H. Deng, Dong-Ling Song, Chao Quantum Physics Quantum computers may outperform classical computers on machine learning tasks. In recent years, a variety of quantum algorithms promising unparalleled potential to enhance, speed up, or innovate machine learning have been proposed. Yet, quantum learning systems, similar to their classical counterparts, may likewise suffer from the catastrophic forgetting problem, where training a model with new tasks would result in a dramatic performance drop for the previously learned ones. This problem is widely believed to be a crucial obstacle to achieving continual learning of multiple sequential tasks. Here, we report an experimental demonstration of quantum continual learning on a fully programmable superconducting processor. In particular, we sequentially train a quantum classifier with three tasks, two about identifying real-life images and the other on classifying quantum states, and demonstrate its catastrophic forgetting through experimentally observed rapid performance drops for prior tasks. To overcome this dilemma, we exploit the elastic weight consolidation strategy and show that the quantum classifier can incrementally learn and retain knowledge across the three distinct tasks, with an average prediction accuracy exceeding 92.3%. In addition, for sequential tasks involving quantum-engineered data, we demonstrate that the quantum classifier can achieve a better continual learning performance than a commonly used classical feedforward network with a comparable number of variational parameters. Our results establish a viable strategy for empowering quantum learning systems with desirable adaptability to multiple sequential tasks, marking an important primary experimental step towards the long-term goal of achieving quantum artificial general intelligence. |
| title | Quantum continual learning on a programmable superconducting processor |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2409.09729 |