Distributed Quantum Inner Product Estimation with Structured Random Circuits

Apr 21, 2026·
Congcong Zheng
Kun WANG
Kun WANG
,
Xutao Yu
,
Ping Xu
,
Zaichen Zhang
· 0 min read
Abstract
Distributed inner product estimation asks two remote platforms to estimate the overlap of unknown quantum states. We prove average sample-complexity bounds for arbitrary unitary 2-designs, brickwork circuits, and local unitary 2-designs, and develop a tensor-network method for state-dependent analysis. Global Clifford sampling matches the scaling of unitary 4-designs, while nonstabilizerness can further improve local and global Clifford protocols. The resulting methods replace difficult random designs with ensembles that are more accessible experimentally.
Type
Publication
npj Quantum Information 12, 94
publication research
Distributed Quantum Estimation Randomized Measurements Structured Random Circuits
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.