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000943317 1001_ $$0P:(DE-Juel1)188131$$aHeadley, David$$b0$$eCorresponding author$$ufzj
000943317 245__ $$aProblem-size-independent angles for a Grover-driven quantum approximate optimization algorithm
000943317 260__ $$aWoodbury, NY$$bInst.$$c2023
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000943317 520__ $$aThe 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.
000943317 536__ $$0G:(DE-HGF)POF4-5214$$a5214 - Quantum State Preparation and Control (POF4-521)$$cPOF4-521$$fPOF IV$$x0
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000943317 7001_ $$0P:(DE-Juel1)184630$$aWilhelm-Mauch, Frank$$b1$$ufzj
000943317 773__ $$0PERI:(DE-600)2844156-4$$a10.1103/PhysRevA.107.012412$$gVol. 107, no. 1, p. 012412$$n1$$p012412$$tPhysical review / A$$v107$$x2469-9926$$y2023
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