Worst-Case Sample Complexity Bounds for Distributed Inner Product Estimation with Local Randomized Measurements

May 14, 2026·
Zhenyuan Huang
Kun WANG
Kun WANG
,
Ping Xu
· 0 min read
Abstract
We study worst-case distributed inner product estimation for n-qubit states using local randomized measurements. For the unique unbiased Hamming-distance kernel under local Clifford sampling, a sharp fourth-moment bound yields sample complexity proportional to the square root of 4.5 to the n. We compare this result with local Haar sampling, formulate a sharper conjectured Haar scaling, verify it for important state classes, and show that independent single-qubit Pauli shadows have a worse asymptotic worst-case scaling.
Type
Publication
arXiv preprint arXiv:2605.14256
publication research
Distributed Quantum Estimation Randomized Measurements Sample 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.