TY  - JOUR
AU  - Headley, David
AU  - Wilhelm-Mauch, Frank
TI  - Problem-size-independent angles for a Grover-driven quantum approximate optimization algorithm
JO  - Physical review / A
VL  - 107
IS  - 1
SN  - 2469-9926
CY  - Woodbury, NY
PB  - Inst.
M1  - FZJ-2023-00923
SP  - 012412
PY  - 2023
AB  - 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.
LB  - PUB:(DE-HGF)16
UR  - <Go to ISI:>//WOS:000975559000001
DO  - DOI:10.1103/PhysRevA.107.012412
UR  - https://juser.fz-juelich.de/record/943317
ER  -