---
res:
  bibo_abstract:
  - 'Combinatorial optimization is a challenging problem applicable in a wide range
    of fields from logistics to finance. Recently, quantum computing has been used
    to attempt to solve these problems using a range of algorithms, including parameterized
    quantum circuits, adiabatic protocols, and quantum annealing. These solutions
    typically have several challenges: 1) there is little to no performance gain over
    classical methods; 2) not all constraints and objectives may be efficiently encoded
    in the quantum ansatz; and 3) the solution domain of the objective function may
    not be the same as the bit strings of measurement outcomes. This work presents
    “nonnative hybrid algorithms”: a framework to overcome these challenges by integrating
    quantum and classical resources with a hybrid approach. By designing nonnative
    quantum variational anosatzes that inherit some but not all problem structure,
    measurement outcomes from the quantum computer can act as a resource to be used
    by classical routines to indirectly compute optimal solutions, partially overcoming
    the challenges of contemporary quantum optimization approaches. These methods
    are demonstrated using a publicly available neutral-atom quantum computer on two
    simple problems of Max k-Cut and maximum independent set. We find improvements
    in solution quality when comparing the hybrid algorithm to its “no quantum” version,
    a demonstration of a “comparative advantage.”@eng'
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Jonathan
      foaf_name: Wurtz, Jonathan
      foaf_surname: Wurtz
  - foaf_Person:
      foaf_givenName: Stefan
      foaf_name: Sack, Stefan
      foaf_surname: Sack
      foaf_workInfoHomepage: http://www.librecat.org/personId=dd622248-f6e0-11ea-865d-ce382a1c81a5
    orcid: 0000-0001-5400-8508
  - foaf_Person:
      foaf_givenName: Sheng-Tao
      foaf_name: Wang, Sheng-Tao
      foaf_surname: Wang
  bibo_doi: 10.1109/tqe.2024.3443660
  bibo_volume: 5
  dct_date: 2024^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/2689-1808
  dct_language: eng
  dct_publisher: IEEE@
  dct_title: Solving nonnative combinatorial optimization problems using hybrid quantum–classical
    algorithms@
...