A quantum-inspired tensor network method for constrained combinatorial optimization problems release_y7iglyelvvde5hgupub4pezywy

by Tianyi Hao and Xuxin Huang and Chunjing Jia and Cheng Peng

Released as a article .

2022  

Abstract

Combinatorial optimization is of general interest for both theoretical study and real-world applications. Fast-developing quantum algorithms provide a different perspective on solving combinatorial optimization problems. In this paper, we propose a quantum inspired algorithm for general locally constrained combinatorial optimization problems by encoding the constraints directly into a tensor network state. The optimal solution can be efficiently solved by borrowing the imaginary time evolution from a quantum many-body system. We demonstrate our algorithm with the open-pit mining problem numerically. Our computational results show the effectiveness of this construction and potential applications in further studies for general combinatorial optimization problems.
In text/plain format

Archived Files and Locations

application/pdf  1.5 MB
file_m3zqdryryzb4blokm4xyvchtw4
arxiv.org (repository)
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  article
Stage   submitted
Date   2022-03-29
Version   v1
Language   en ?
arXiv  2203.15246v1
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: 7cf13ec1-7514-47c5-a7e0-fdde6c0af088
API URL: JSON