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Improved Algorithms for Quantum MaxCut via Partially Entangled Matchings

Year: 2025

Type: preprint

Abstract: We introduce a $0.611$-approximation algorithm for Quantum MaxCut and a $\frac{1+\sqrt{5}}{4} \approx 0.809$-approximation algorithm for the EPR Hamiltonian of [arXiv:2209.02589]. A novel ingredient in both of these algorithms is to partially entangle pairs of qubits associated to edges in a matching, while preserving the direction of their single-qubit Bloch vectors. This allows us to interpolate between product states and matching-based states with a tunable parameter.

Source: arXiv (Cornell University)

Authors: Apte, Anuj, Lee, Eunou, Marwaha, Kunal, Parekh, Ojas, Sud, James

Institutions: University of Chicago, Korea Institute for Advanced Study, Sandia National Laboratories

Language: English

Topic: Quantum Computing Algorithms and Architecture

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, U.S. Department of Energy, University of Chicago, Office of Science, Advanced Scientific Computing Research, Air Force Office of Scientific Research, Sandia National Laboratories

Awards: G11630931, G1166951225, G1523888516, G2214935549, G280414146, Graduate Research Fellowship Program (GRFP), G413247060, G4516736450, G4578494242, G4771968897, G4903105778, G5556147165, G6338696783

OpenAlex ID: W6979352434

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