Optimal Distributed Similarity Estimation of Quantum Channels

Dec 11, 2025·
Congcong Zheng
Kun WANG
Kun WANG
,
Xutao Yu
,
Ping Xu
,
Zaichen Zhang
· 0 min read
Abstract
We formulate distributed similarity estimation of quantum channels through the normalized inner product of their Choi states. The optimal query complexity is the maximum of the square root of the channel dimension divided by the additive error and the inverse squared error. The lower bound permits adaptive coherent strategies, ancillas, and multiple communication rounds, while the matching randomized algorithm is nonadaptive and ancilla-free. The algorithm also provides a quadratic improvement over classical-shadow baselines.
Type
Publication
arXiv preprint arXiv:2512.10465
publication research
Distributed Quantum Estimation Quantum Channels Query Complexity
Kun WANG
Authors
Kun WANG
Associate Researcher

I am an Associate Researcher and Outstanding Young Talent in the College of Computer Science and Technology, National University of Defense Technology (NUDT).

My research develops practical foundations for reliable and scalable quantum information processing. I work across photonic quantum computing, quantum characterization, verification and validation, distributed quantum estimation, and quantum information theory.

Before joining NUDT, I was a Senior Researcher at the Institute for Quantum Computing, Baidu Research, from 2020 to 2023. I led the development of the Quantum Error Processing (QEP) toolkit for characterizing, mitigating, and correcting errors in quantum devices through software. I received the Shenzhen Industrial Development and Innovation Talent Award in 2023.

Previously, I was a postdoc at the Shenzhen Institute for Quantum Science and Engineering (SIQSE), Southern University of Science and Technology, where I worked with Prof. Masahito Hayashi.