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 accuracy exceeding 92.3%. Additionally, for sequential tasks involving quantum-engineered data, we demonstrate that the quantum classifier outperforms a classical classifier with a comparable number of parameters. Our results establish a viable strategy for empowering quantum learning systems with adaptability to sequential tasks.
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Data availability
All data and codes needed to evaluate the conclusions in the paper are archived in Zenodo: https://doi.org/10.5281/zenodo.17669105.
Code availability
All codes needed to evaluate the conclusions in the paper are archived in Zenodo: https://doi.org/10.5281/zenodo.17669105.
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Acknowledgements
We thank J. Eisert, M. Hafezi, D. Yuan, and S. Jiang for helpful discussions. The device was fabricated at the Micro-Nano Fabrication Center of Zhejiang University. We acknowledge support from the Quantum Science and Technology-National Science and Technology Major Project (Grant Nos. 2021ZD0300200 and 2021ZD0302203), the National Natural Science Foundation of China (Grant Nos. 12174342, 92365301, 12274367, 12322414, 12274368, 12075128, and T2225008), the National Key R&D Program of China (Grant No. 2023YFB4502600), and the Zhejiang Provincial Natural Science Foundation of China (Grant Nos. LDQ23A040001, LR24A040002). Z.L., W.L., W.J., Z.-Z.S., and D.-L.D. are supported in addition by Tsinghua University Dushi Program, and the Shanghai Qi Zhi Institute Innovation Program (Grant No. SQZ202318). C.S. is supported by the Xiaomi Young Scholars Program. P.-X.S. acknowledges support from the European Union's Horizon Europe research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 101180589 (SymPhysAI), the National Science Centre (Poland) OPUS Grant No. 2021/41/B/ST3/04475, and the Foundation for Polish Science project MagTop (No. FENG.02.01-IP.05-0028/23) co-financed by the European Union from the funds of Priority 2 of the European Funds for a Smart Economy Program 2021–2027 (FENG). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.
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C.Z. carried out the experiments and analyzed the data with the assistance of S.X., K.W., J.C., Y.W., F.J., X.Z., Y.G., Z.T., Z.C., A.Z., N.W., Y.Z., T.L., F.S., J.Z., Z.B., Z.Z., Z.S., J.D., H.D., P.Z., H.L., Q.G., Z.W.; C.S. and H.W. directed the experiments; Z.L. formalized the theoretical framework and performed the numerical simulations under the supervision of D.-L.D.; W.L., W.J., Z.-Z.S. and P.-X.S. provided theoretical support; J.C. and X.Z. designed the device; H.L. fabricated the device, supervised by H.W.; L.Z. and J.H. provided further experimental support; C.Z., Z.L., J.H., H.W., D.-L.D., and C.S. wrote the manuscript with feedback from all authors.
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Zhang, C., Lu, Z., Zhao, L. et al. Experimental demonstration of quantum continual learning with superconducting qubits. npj Quantum Inf (2026). https://doi.org/10.1038/s41534-025-01174-y
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DOI: https://doi.org/10.1038/s41534-025-01174-y
