Performance Analysis of Multi-Angle QAOA for p > 1
Fuente:
arXiv
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911988794261504 |
|---|---|
| author | Gaidai, Igor Herrman, Rebekah |
| author_facet | Gaidai, Igor Herrman, Rebekah |
| contents | In this paper we consider the scalability of Multi-Angle QAOA with respect to the number of QAOA layers. We found that MA-QAOA is able to significantly reduce the depth of QAOA circuits, by a factor of up to 4 for the considered data sets. However, MA-QAOA is not optimal for minimization of the total QPU time. Different optimization initialization strategies are considered and compared for both QAOA and MA-QAOA. Among them, a new initialization strategy is suggested for MA-QAOA that is able to consistently and significantly outperform random initialization used in the previous studies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_00200 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Performance Analysis of Multi-Angle QAOA for p > 1 Gaidai, Igor Herrman, Rebekah Emerging Technologies Quantum Physics In this paper we consider the scalability of Multi-Angle QAOA with respect to the number of QAOA layers. We found that MA-QAOA is able to significantly reduce the depth of QAOA circuits, by a factor of up to 4 for the considered data sets. However, MA-QAOA is not optimal for minimization of the total QPU time. Different optimization initialization strategies are considered and compared for both QAOA and MA-QAOA. Among them, a new initialization strategy is suggested for MA-QAOA that is able to consistently and significantly outperform random initialization used in the previous studies. |
| title | Performance Analysis of Multi-Angle QAOA for p > 1 |
| topic | Emerging Technologies Quantum Physics |
| url | https://arxiv.org/abs/2312.00200 |