Deep reinforcement learning for near-deterministic preparation of cubic- and quartic-phase gates in photonic quantum computing
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866909036718325760 |
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| author | Anteneh, Amanuel Brunel, Léandre González-Arciniegas, Carlos Pfister, Olivier |
| author_facet | Anteneh, Amanuel Brunel, Léandre González-Arciniegas, Carlos Pfister, Olivier |
| contents | Cubic-phase states are a sufficient resource for universal quantum computing over continuous variables. We present results from numerical experiments in which deep neural networks are trained via reinforcement learning to control a quantum optical circuit for generating cubic-phase states, with an average success rate of 96%. The only non-Gaussian resource required is photon-number-resolving measurements. We also show that the exact same resources enable the direct generation of a quartic-phase gate, with no need for a cubic gate decomposition. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_07859 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deep reinforcement learning for near-deterministic preparation of cubic- and quartic-phase gates in photonic quantum computing Anteneh, Amanuel Brunel, Léandre González-Arciniegas, Carlos Pfister, Olivier Quantum Physics Machine Learning Cubic-phase states are a sufficient resource for universal quantum computing over continuous variables. We present results from numerical experiments in which deep neural networks are trained via reinforcement learning to control a quantum optical circuit for generating cubic-phase states, with an average success rate of 96%. The only non-Gaussian resource required is photon-number-resolving measurements. We also show that the exact same resources enable the direct generation of a quartic-phase gate, with no need for a cubic gate decomposition. |
| title | Deep reinforcement learning for near-deterministic preparation of cubic- and quartic-phase gates in photonic quantum computing |
| topic | Quantum Physics Machine Learning |
| url | https://arxiv.org/abs/2506.07859 |