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@ARTICLE{Headley:943317,
author = {Headley, David and Wilhelm-Mauch, Frank},
title = {{P}roblem-size-independent angles for a {G}rover-driven
quantum approximate optimization algorithm},
journal = {Physical review / A},
volume = {107},
number = {1},
issn = {2469-9926},
address = {Woodbury, NY},
publisher = {Inst.},
reportid = {FZJ-2023-00923},
pages = {012412},
year = {2023},
abstract = {The quantum approximate optimization algorithm (QAOA)
requires that circuit parameters are determined that allow
one to sample from high-quality solutions to combinatorial
optimization problems. Such parameters can be obtained using
either costly outer-loop optimization procedures and
repeated calls to a quantum computer or, alternatively, via
analytical means. In this work, we consider a context in
which one knows a probability density function describing
how the objective function of a combinatorial optimization
problem is distributed. We show that, if one knows this
distribution, then the expected value of strings, sampled by
measuring a Grover-driven, QAOA-prepared state, can be
calculated independently of the size of the problem in
question. By optimizing this quantity, optimal circuit
parameters for average-case problems can be obtained on a
classical computer. Such calculations can help deliver
insights into the performance of and predictability of
angles in QAOA in the limit of large problem sizes, in
particular, for the number partitioning problem.},
cin = {PGI-12},
ddc = {530},
cid = {I:(DE-Juel1)PGI-12-20200716},
pnm = {5214 - Quantum State Preparation and Control (POF4-521)},
pid = {G:(DE-HGF)POF4-5214},
typ = {PUB:(DE-HGF)16},
UT = {WOS:000975559000001},
doi = {10.1103/PhysRevA.107.012412},
url = {https://juser.fz-juelich.de/record/943317},
}