Photonic counterdiabatic quantum optimization algorithm
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866929522861932544 |
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| author | Chandarana, Pranav Paul, Koushik Garcia-de-Andoin, Mikel Ban, Yue Sanz, Mikel Chen, Xi |
| author_facet | Chandarana, Pranav Paul, Koushik Garcia-de-Andoin, Mikel Ban, Yue Sanz, Mikel Chen, Xi |
| contents | We propose a hybrid quantum-classical approximate optimization algorithm for photonic quantum computing, specifically tailored for addressing continuous-variable optimization problems. Inspired by counterdiabatic protocols, our algorithm significantly reduces the required quantum operations for optimization as compared to adiabatic protocols. This reduction enables us to tackle non-convex continuous optimization and countably infinite integer programming within the near-term era of quantum computing. Through comprehensive benchmarking, we demonstrate that our approach outperforms existing state-of-the-art hybrid adiabatic quantum algorithms in terms of convergence and implementability. Remarkably, our algorithm offers a practical and accessible experimental realization, bypassing the need for high-order operations and overcoming experimental constraints. We conduct proof-of-principle experiments on an eight-mode nanophotonic quantum chip, successfully showcasing the feasibility and potential impact of the algorithm. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2307_14853 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Photonic counterdiabatic quantum optimization algorithm Chandarana, Pranav Paul, Koushik Garcia-de-Andoin, Mikel Ban, Yue Sanz, Mikel Chen, Xi Quantum Physics Optics We propose a hybrid quantum-classical approximate optimization algorithm for photonic quantum computing, specifically tailored for addressing continuous-variable optimization problems. Inspired by counterdiabatic protocols, our algorithm significantly reduces the required quantum operations for optimization as compared to adiabatic protocols. This reduction enables us to tackle non-convex continuous optimization and countably infinite integer programming within the near-term era of quantum computing. Through comprehensive benchmarking, we demonstrate that our approach outperforms existing state-of-the-art hybrid adiabatic quantum algorithms in terms of convergence and implementability. Remarkably, our algorithm offers a practical and accessible experimental realization, bypassing the need for high-order operations and overcoming experimental constraints. We conduct proof-of-principle experiments on an eight-mode nanophotonic quantum chip, successfully showcasing the feasibility and potential impact of the algorithm. |
| title | Photonic counterdiabatic quantum optimization algorithm |
| topic | Quantum Physics Optics |
| url | https://arxiv.org/abs/2307.14853 |