Literature Database Entry

tao2026distributed

Youming Tao, Yaowu Yang, Shuzhen Chen, Huiqun Li, Congwei Zhang and Falko Dressler, "Distributed PAC Learning from Quantum Data with Efficient Communication," Proceedings of 20th International Conference on Wireless Artificial Intelligent Computing Systems and Applications (WASA 2026), Xi’an, China, June 2026. (to appear)

Abstract

We study distributed PAC learning from quantum data under classical communication constraints, where multiple clients collaboratively learn classical patterns from labeled quantum states. The key question is what collaboration can fundamentally gain in this setting. We show that collaboration can reduce the per-client quantum sample complexity by a linear factor of 1/K for achieving the same target error as centralized QPAC learning. To establish this result, we first propose DQERM-AVG, a distributed quantum empirical risk minimization algorithm that achieves this per-client reduction. We then characterize the communication required to obtain such a gain by proving a lower bound of ω(|C|+ K) within our measurement-partitioning framework. Motivated by this result, we further develop DQERM-RR, which preserves the same per-client sample complexity while reducing the communication cost from O(|C|K) to the optimal O(|C|+ K). Experiments on a quantum state classification task further support the theoretical findings.

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Youming Tao
Yaowu Yang
Shuzhen Chen
Huiqun Li
Congwei Zhang
Falko Dressler

BibTeX reference

@inproceedings{tao2026distributed,
    author = {Tao, Youming and Yang, Yaowu and Chen, Shuzhen and Li, Huiqun and Zhang, Congwei and Dressler, Falko},
    note = {to appear},
    title = {{Distributed PAC Learning from Quantum Data with Efficient Communication}},
    publisher = {Springer},
    address = {Xi’an, China},
    booktitle = {20th International Conference on Wireless Artificial Intelligent Computing Systems and Applications (WASA 2026)},
    month = {6},
    year = {2026},
   }
   
   

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Last modified: 2026-06-08