Work

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

Year: 2022

Type: article

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 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.

Source: Frontiers in Physics

DOI: 10.3389/fphy.2022.906590

Authors: Tianyi Hao, Xuxin Huang, Chunjing Jia, Cheng Peng

Institutions: Stanford University, SLAC National Accelerator Laboratory, University of Florida

Language: English

Topic: Quantum Computing Algorithms and Architecture

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,490

Funders: U.S. Department of Energy, Office of Science, Basic Energy Sciences