Kavli Affiliate: Cheng Peng
| First 5 Authors: Tianyi Hao, Xuxin Huang, Chunjing Jia, Cheng Peng,
| Summary:
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.
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