Bipartite Gaussian Boson Sampling for Hamiltonian Cycles in Directed Graphs

Jun 27, 2026·
Miaomiao Yu
,
Jingyi Lv
,
Yan Wang
Kun WANG
Kun WANG
,
Ping Xu
· 0 min read
Abstract
Bipartite Gaussian boson sampling produces probabilities governed by squared permanents of submatrices of arbitrary complex matrices, matching the nonsymmetric structure of directed graphs. We formulate Max-Perm as a canonical optimization problem, derive its sampling enhancement over uniform sampling, and use permanent-biased samples to guide a genetic algorithm for directed Hamiltonian cycles. Numerical experiments on random directed graphs show higher success rates and longer valid paths, with guided initialization providing the largest contribution.
Type
Publication
arXiv preprint arXiv:2606.28775
publication research
Photonic Quantum Computing Gaussian Boson Sampling Directed Graphs Hamiltonian Cycles
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.