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A quantum-inspired tensor network method for constrained combinatorial optimization problems
[article]
2022
arXiv
pre-print
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
arXiv:2203.15246v1
fatcat:y7iglyelvvde5hgupub4pezywy