Optimal Quantum Circuit Design via Unitary Neural Networks
Fuente:
arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909294242299904 |
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| author | Zomorodi, M. Amini, H. Abbaszadeh, M. Sohrabi, J. Salari, V. Plawiak, P. |
| author_facet | Zomorodi, M. Amini, H. Abbaszadeh, M. Sohrabi, J. Salari, V. Plawiak, P. |
| contents | The process of translating a quantum algorithm into a form suitable for implementation on a quantum computing platform is crucial but yet challenging. This entails specifying quantum operations with precision, a typically intricate task. In this paper, we present an alternative approach: an automated method for synthesizing the functionality of a quantum algorithm into a quantum circuit model representation. Our methodology involves training a neural network model using diverse input-output mappings of the quantum algorithm. We demonstrate that this trained model can effectively generate a quantum circuit model equivalent to the original algorithm. Remarkably, our observations indicate that the trained model achieves near-perfect mapping of unseen inputs to their respective outputs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_13211 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Optimal Quantum Circuit Design via Unitary Neural Networks Zomorodi, M. Amini, H. Abbaszadeh, M. Sohrabi, J. Salari, V. Plawiak, P. Quantum Physics Artificial Intelligence The process of translating a quantum algorithm into a form suitable for implementation on a quantum computing platform is crucial but yet challenging. This entails specifying quantum operations with precision, a typically intricate task. In this paper, we present an alternative approach: an automated method for synthesizing the functionality of a quantum algorithm into a quantum circuit model representation. Our methodology involves training a neural network model using diverse input-output mappings of the quantum algorithm. We demonstrate that this trained model can effectively generate a quantum circuit model equivalent to the original algorithm. Remarkably, our observations indicate that the trained model achieves near-perfect mapping of unseen inputs to their respective outputs. |
| title | Optimal Quantum Circuit Design via Unitary Neural Networks |
| topic | Quantum Physics Artificial Intelligence |
| url | https://arxiv.org/abs/2408.13211 |