2022
DOI: 10.48550/arxiv.2205.01192
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Augmenting QAOA Ansatz with Multiparameter Problem-Independent Layer

Abstract: The quantum approximate optimization algorithm (QAOA) promises to solve classically intractable computational problems in the area of combinatorial optimization. A growing amount of evidence suggests that the originally proposed form of the QAOA ansatz is not optimal, however. To address this problem, we propose an alternative ansatz, which we call QAOA+, that augments the traditional p = 1 QAOA ansatz with an additional multiparameter problem-independent layer. The QAOA+ ansatz allows obtaining higher approxi… Show more

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Cited by 1 publication
(2 citation statements)
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“…Several variants of the original QAOA algorithm have been developed, each with different operators and initial states [14][15][16][17][18][19][20][21][22][23][24][25] or different objective functions for tuning the variational parameters [26,27]. Depth-reduction techniques [28,29] or methods like circuit cutting [30,31] that optimise QAOA circuits while taking into account quantum hardware limitations; as well as classical aspects such as hyper-parameter optimisation and exploitation of problem structure, have been studied as well [18,19,[32][33][34][35][36][37][38][39].…”
Section: Introductionmentioning
confidence: 99%
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“…Several variants of the original QAOA algorithm have been developed, each with different operators and initial states [14][15][16][17][18][19][20][21][22][23][24][25] or different objective functions for tuning the variational parameters [26,27]. Depth-reduction techniques [28,29] or methods like circuit cutting [30,31] that optimise QAOA circuits while taking into account quantum hardware limitations; as well as classical aspects such as hyper-parameter optimisation and exploitation of problem structure, have been studied as well [18,19,[32][33][34][35][36][37][38][39].…”
Section: Introductionmentioning
confidence: 99%
“…There are several approaches that have been proposed to improve the performance of low-depth QAOA by adding new parameters to the ansatz [23,[56][57][58][59][60]. These approaches include Multi-Angle QAOA (MA-QAOA) [56], which increases the number of classical parameters added in each layer for more precise control of the optimisation process; Free-Axis Mixer QAOA (FAM-QAOA) [57], which includes additional variational parameters in the mixer Hamiltonian that allow for rotation about an axis in the XY plane; QAOA with Adaptive Bias Fields (AB-QAOA) [58], which adds a Pauli-Z component to the mixer Hamiltonian; Adaptive Derivative Assembled Problem Tailored QAOA (ADAPT-QAOA) [59], which grows the ansatz iteratively using a gradient criterion; and QAOA+ [23], which augments the traditional QAOA ansatz with an additional multi-parameter layer that is independent of the specific problem being solved. Despite these improvements, there is still a need for a low-computational overhead ansatz that is tailored to specific problems and allows for greater flexibility in the optimisation process.…”
Section: Introductionmentioning
confidence: 99%