Evolving a multi-population evolutionary-QAOA on distributed QPUs
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913935450439680 |
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| author | Schiavello, Francesca Altamura, Edoardo Tavernelli, Ivano Mensa, Stefano Symons, Benjamin |
| author_facet | Schiavello, Francesca Altamura, Edoardo Tavernelli, Ivano Mensa, Stefano Symons, Benjamin |
| contents | Our work integrates an Evolutionary Algorithm (EA) with the Quantum Approximate Optimization Algorithm (QAOA) to optimize ansatz parameters in place of traditional gradient-based methods. We benchmark this Evolutionary-QAOA (E-QAOA) approach on the Max-Cut problem for $d$-3 regular graphs of 4 to 26 nodes, demonstrating equal or higher accuracy and reduced variance compared to COBYLA-based QAOA, especially when using Conditional Value at Risk (CVaR) for fitness evaluations. Additionally, we propose a novel distributed multi-population EA strategy, executing parallel, independent populations on two quantum processing units (QPUs) with classical communication of 'elite' solutions. Experiments on quantum simulators and IBM hardware validate the approach. We also discuss potential extensions of our method and outline promising future directions in scalable, distributed quantum optimization on hybrid quantum-classical infrastructures. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_10739 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Evolving a multi-population evolutionary-QAOA on distributed QPUs Schiavello, Francesca Altamura, Edoardo Tavernelli, Ivano Mensa, Stefano Symons, Benjamin Quantum Physics Neural and Evolutionary Computing Our work integrates an Evolutionary Algorithm (EA) with the Quantum Approximate Optimization Algorithm (QAOA) to optimize ansatz parameters in place of traditional gradient-based methods. We benchmark this Evolutionary-QAOA (E-QAOA) approach on the Max-Cut problem for $d$-3 regular graphs of 4 to 26 nodes, demonstrating equal or higher accuracy and reduced variance compared to COBYLA-based QAOA, especially when using Conditional Value at Risk (CVaR) for fitness evaluations. Additionally, we propose a novel distributed multi-population EA strategy, executing parallel, independent populations on two quantum processing units (QPUs) with classical communication of 'elite' solutions. Experiments on quantum simulators and IBM hardware validate the approach. We also discuss potential extensions of our method and outline promising future directions in scalable, distributed quantum optimization on hybrid quantum-classical infrastructures. |
| title | Evolving a multi-population evolutionary-QAOA on distributed QPUs |
| topic | Quantum Physics Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2409.10739 |