License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.APPROX/RANDOM.2020.59
URN: urn:nbn:de:0030-drops-126629
URL: https://drops.dagstuhl.de/opus/volltexte/2020/12662/
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Hallgren, Sean ; Lee, Eunou ; Parekh, Ojas

An Approximation Algorithm for the MAX-2-Local Hamiltonian Problem

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LIPIcs-APPROX59.pdf (0.4 MB)

Abstract

We present a classical approximation algorithm for the MAX-2-Local Hamiltonian problem. This is a maximization version of the QMA-complete 2-Local Hamiltonian problem in quantum computing, with the additional assumption that each local term is positive semidefinite. The MAX-2-Local Hamiltonian problem generalizes NP-hard constraint satisfaction problems, and our results may be viewed as generalizations of approximation approaches for the MAX-2-CSP problem. We work in the product state space and extend the framework of Goemans and Williamson for approximating MAX-2-CSPs. The key difference is that in the product state setting, a solution consists of a set of normalized 3-dimensional vectors rather than boolean numbers, and we leverage approximation results for rank-constrained Grothendieck inequalities. For MAX-2-Local Hamiltonian we achieve an approximation ratio of 0.328. This is the first example of an approximation algorithm beating the random quantum assignment ratio of 0.25 by a constant factor.

BibTeX - Entry

@InProceedings{hallgren_et_al:LIPIcs:2020:12662,
  author =	{Sean Hallgren and Eunou Lee and Ojas Parekh},
  title =	{{An Approximation Algorithm for the MAX-2-Local Hamiltonian Problem}},
  booktitle =	{Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2020)},
  pages =	{59:1--59:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-164-1},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{176},
  editor =	{Jaros{\l}aw Byrka and Raghu Meka},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2020/12662},
  URN =		{urn:nbn:de:0030-drops-126629},
  doi =		{10.4230/LIPIcs.APPROX/RANDOM.2020.59},
  annote =	{Keywords: approximation algorithm, quantum computing, local Hamiltonian, mean-field theory, randomized rounding}
}

Keywords: approximation algorithm, quantum computing, local Hamiltonian, mean-field theory, randomized rounding
Collection: Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2020)
Issue Date: 2020
Date of publication: 11.08.2020

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