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

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

doi:10.3389/fphy.2022.906590

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

Journal Article · Fri Jul 08 00:00:00 EDT 2022 · Frontiers in Physics

DOI:https://doi.org/10.3389/fphy.2022.906590· OSTI ID:1872978

Hao, Tianyi ^[1]; Huang, Xuxin ^[2]; Jia, Chunjing ^[3]; Peng, Cheng ^[4]

Stanford Univ., CA (United States); Stanford Institute for Materials and Energy Sciences, SLAC National Lab
Stanford Univ., CA (United States); SLAC National Accelerator Lab., Menlo Park, CA (United States)
SLAC National Accelerator Lab., Menlo Park, CA (United States); Univ. of Florida, Gainesville, FL (United States)
SLAC National Accelerator Lab., Menlo Park, CA (United States)

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.

View Accepted Manuscript (DOE)

Research Organization:: SLAC National Accelerator Laboratory, Menlo Park, CA (United States). Stanford Institute for Materials and Energy Sciences

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES). Materials Sciences & Engineering Division

Grant/Contract Number:: AC02-76SF00515

OSTI ID:: 1872978

Journal Information:: Frontiers in Physics, Journal Name: Frontiers in Physics Vol. 10; ISSN 2296-424X

Publisher:: Frontiers Research FoundationCopyright Statement

Country of Publication:: United States

Language:: English

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71 CLASSICAL AND QUANTUM MECHANICS
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A Quantum-Inspired Tensor Network Algorithm for Constrained Combinatorial Optimization Problems

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References (19)

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