Work

Variational matrix product states for combinatorial optimization

Year: 2026

Type: article

Abstract: To compute approximate solutions for combinatorial optimization problems, we describe variational methods based on the product state (PS) and matrix product state (MPS) ansätze. We perform variational energy minimization with respect to a quantum annealing Hamiltonian and utilize randomness by embedding the approaches in the metaheuristic iterated local search (ILS). The resulting quantum-inspired ILS algorithms are benchmarked on maximum cut problems of up to 50 000 variables. We show that they can outperform traditional (M)PS methods, classical ILS, the quantum approximate optimization algorithm, and other variational quantum-inspired solvers.

Source: Physical Review Research

DOI: 10.1103/x7s3-ykb8

Authors: Guillermo Preisser, Conor Mc Keever, Michael Lubasch

Institutions: Quantinuum (United Kingdom), Quantinuum (United States)

Language: English

Topic: Quantum Computing Algorithms and Architecture

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,625

OpenAlex ID: W7164000710

FWCI:

Cites: 54

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Related to: 10