Details

Autor(en) / Beteiligte

• Hao, Tianyi
• Huang, Xuxin
• Jia, Chunjing
• Peng, Cheng

Titel

A Quantum-Inspired Tensor Network Algorithm for Constrained Combinatorial Optimization Problems

Ist Teil von

• Frontiers in physics, 2022-07, Vol.10

Ort / Verlag

United States: Frontiers Research Foundation

Erscheinungsjahr

2022

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• 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 tensor-network-based algorithm for general locally constrained combinatorial optimization problems. Our algorithm constructs a Hamiltonian for the problem of interest, effectively mapping it to a quantum problem, then encodes the constraints directly into a tensor network state and solves the optimal solution by evolving the system to the ground state of the Hamiltonian. We demonstrate our algorithm with the open-pit mining problem, which results in a quadratic asymptotic time complexity. Our numerical results show the effectiveness of this construction and potential applications in further studies for general combinatorial optimization problems.