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A Quantum Approximate Optimization Algorithm Applied to a Bounded Occurrence Constraint Problem

Quantum Physics 2015-06-26 v2

Abstract

We apply our recent Quantum Approximate Optimization Algorithm to the combinatorial problem of bounded occurrence Max E3LIN2. The input is a set of linear equations each of which contains exactly three boolean variables and each equation says that the sum of the variables mod 2 is 0 or is 1. Every variable is in no more than D equations. A random string will satisfy 1/2 of the equations. We show that the level one QAOA will efficiently produce a string that satisfies (12+1101D1/2lnD)\left(\frac{1}{2} + \frac{1}{101 D^{1/2}\, l n\, D}\right) times the number of equations. A recent classical algorithm achieved (12+constantD1/2)\left(\frac{1}{2} + \frac{constant}{D^{1/2}}\right). We also show that in the typical case the quantum computer will output a string that satisfies (12+123eD1/2)\left(\frac{1}{2}+ \frac{1}{2\sqrt{3e}\, D^{1/2}}\right) times the number of equations.

Keywords

Cite

@article{arxiv.1412.6062,
  title  = {A Quantum Approximate Optimization Algorithm Applied to a Bounded Occurrence Constraint Problem},
  author = {Edward Farhi and Jeffrey Goldstone and Sam Gutmann},
  journal= {arXiv preprint arXiv:1412.6062},
  year   = {2015}
}

Comments

This version contains a tighter analysis that leads to stronger results on the performance of the quantum algorithm

R2 v1 2026-06-22T07:37:20.161Z