Quantum noise modeling through Reinforcement Learning
Fuente:
arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| Sujets: | |
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| _version_ | 1866911457511211008 |
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| author | Bordoni, Simone Papaluca, Andrea Buttarini, Piergiorgio Sopena, Alejandro Giagu, Stefano Carrazza, Stefano |
| author_facet | Bordoni, Simone Papaluca, Andrea Buttarini, Piergiorgio Sopena, Alejandro Giagu, Stefano Carrazza, Stefano |
| contents | In the current era of quantum computing, robust and efficient tools are essential to bridge the gap between simulations and quantum hardware execution. In this work, we introduce a machine learning approach to characterize the noise impacting a quantum chip and emulate it during simulations. Our algorithm leverages reinforcement learning, offering increased flexibility in reproducing various noise models compared to conventional techniques such as randomized benchmarking or heuristic noise models. The effectiveness of the RL agent has been validated through simulations and testing on real superconducting qubits. Additionally, we provide practical use-case examples for the study of renowned quantum algorithms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_01506 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Quantum noise modeling through Reinforcement Learning Bordoni, Simone Papaluca, Andrea Buttarini, Piergiorgio Sopena, Alejandro Giagu, Stefano Carrazza, Stefano Quantum Physics Computational Physics In the current era of quantum computing, robust and efficient tools are essential to bridge the gap between simulations and quantum hardware execution. In this work, we introduce a machine learning approach to characterize the noise impacting a quantum chip and emulate it during simulations. Our algorithm leverages reinforcement learning, offering increased flexibility in reproducing various noise models compared to conventional techniques such as randomized benchmarking or heuristic noise models. The effectiveness of the RL agent has been validated through simulations and testing on real superconducting qubits. Additionally, we provide practical use-case examples for the study of renowned quantum algorithms. |
| title | Quantum noise modeling through Reinforcement Learning |
| topic | Quantum Physics Computational Physics |
| url | https://arxiv.org/abs/2408.01506 |