MLQAOA: Graph Learning Accelerated Hybrid Quantum-Classical Multilevel QAOA
Fuente:
arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866908268880723968 |
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| author | Bach, Bao Falla, Jose Safro, Ilya |
| author_facet | Bach, Bao Falla, Jose Safro, Ilya |
| contents | Learning the problem structure at multiple levels of coarseness to inform the decomposition-based hybrid quantum-classical combinatorial optimization solvers is a promising approach to scaling up variational approaches. We introduce a multilevel algorithm reinforced with the spectral graph representation learning-based accelerator to tackle large-scale graph maximum cut instances and fused with several versions of the quantum approximate optimization algorithm (QAOA) and QAOA-inspired algorithms. The graph representation learning model utilizes the idea of QAOA variational parameters concentration and substantially improves the performance of QAOA. We demonstrate the potential of using multilevel QAOA and representation learning-based approaches on very large graphs by achieving high-quality solutions in a much faster time. Reproducibility: Our source code and results are available at https://github.com/bachbao/MLQAOA |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_14399 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MLQAOA: Graph Learning Accelerated Hybrid Quantum-Classical Multilevel QAOA Bach, Bao Falla, Jose Safro, Ilya Quantum Physics Computational Engineering, Finance, and Science Computational Physics Learning the problem structure at multiple levels of coarseness to inform the decomposition-based hybrid quantum-classical combinatorial optimization solvers is a promising approach to scaling up variational approaches. We introduce a multilevel algorithm reinforced with the spectral graph representation learning-based accelerator to tackle large-scale graph maximum cut instances and fused with several versions of the quantum approximate optimization algorithm (QAOA) and QAOA-inspired algorithms. The graph representation learning model utilizes the idea of QAOA variational parameters concentration and substantially improves the performance of QAOA. We demonstrate the potential of using multilevel QAOA and representation learning-based approaches on very large graphs by achieving high-quality solutions in a much faster time. Reproducibility: Our source code and results are available at https://github.com/bachbao/MLQAOA |
| title | MLQAOA: Graph Learning Accelerated Hybrid Quantum-Classical Multilevel QAOA |
| topic | Quantum Physics Computational Engineering, Finance, and Science Computational Physics |
| url | https://arxiv.org/abs/2404.14399 |