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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2409.03861 |
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| _version_ | 1866911060882096128 |
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| author | Nola, Jaden Sanchez, Uriah Murthy, Anusha Krishna Behrman, Elizabeth Steck, James |
| author_facet | Nola, Jaden Sanchez, Uriah Murthy, Anusha Krishna Behrman, Elizabeth Steck, James |
| contents | A gate sequence of single-qubit transformations may be condensed into a single microwave pulse that maps a qubit from an initialized state directly into the desired state of the composite transformation. Here, machine learning is used to learn the parameterized values for a single driving pulse associated with a transformation of three sequential gate operations on a qubit. This implies that future quantum circuits may contain roughly a third of the number of single-qubit operations performed, greatly reducing the problems of noise and decoherence. There is a potential for even greater condensation and efficiency using the methods of quantum machine learning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_03861 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Training microwave pulses using quantum machine learning Nola, Jaden Sanchez, Uriah Murthy, Anusha Krishna Behrman, Elizabeth Steck, James Quantum Physics A gate sequence of single-qubit transformations may be condensed into a single microwave pulse that maps a qubit from an initialized state directly into the desired state of the composite transformation. Here, machine learning is used to learn the parameterized values for a single driving pulse associated with a transformation of three sequential gate operations on a qubit. This implies that future quantum circuits may contain roughly a third of the number of single-qubit operations performed, greatly reducing the problems of noise and decoherence. There is a potential for even greater condensation and efficiency using the methods of quantum machine learning. |
| title | Training microwave pulses using quantum machine learning |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2409.03861 |