Learning to Decode the Surface Code with a Recurrent, Transformer-Based Neural Network
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arXiv
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| Autori principali: | , , , , , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866912146877579264 |
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| author | Bausch, Johannes Senior, Andrew W Heras, Francisco J H Edlich, Thomas Davies, Alex Newman, Michael Jones, Cody Satzinger, Kevin Niu, Murphy Yuezhen Blackwell, Sam Holland, George Kafri, Dvir Atalaya, Juan Gidney, Craig Hassabis, Demis Boixo, Sergio Neven, Hartmut Kohli, Pushmeet |
| author_facet | Bausch, Johannes Senior, Andrew W Heras, Francisco J H Edlich, Thomas Davies, Alex Newman, Michael Jones, Cody Satzinger, Kevin Niu, Murphy Yuezhen Blackwell, Sam Holland, George Kafri, Dvir Atalaya, Juan Gidney, Craig Hassabis, Demis Boixo, Sergio Neven, Hartmut Kohli, Pushmeet |
| contents | Quantum error-correction is a prerequisite for reliable quantum computation. Towards this goal, we present a recurrent, transformer-based neural network which learns to decode the surface code, the leading quantum error-correction code. Our decoder outperforms state-of-the-art algorithmic decoders on real-world data from Google's Sycamore quantum processor for distance 3 and 5 surface codes. On distances up to 11, the decoder maintains its advantage on simulated data with realistic noise including cross-talk, leakage, and analog readout signals, and sustains its accuracy far beyond the 25 cycles it was trained on. Our work illustrates the ability of machine learning to go beyond human-designed algorithms by learning from data directly, highlighting machine learning as a strong contender for decoding in quantum computers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_05900 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Learning to Decode the Surface Code with a Recurrent, Transformer-Based Neural Network Bausch, Johannes Senior, Andrew W Heras, Francisco J H Edlich, Thomas Davies, Alex Newman, Michael Jones, Cody Satzinger, Kevin Niu, Murphy Yuezhen Blackwell, Sam Holland, George Kafri, Dvir Atalaya, Juan Gidney, Craig Hassabis, Demis Boixo, Sergio Neven, Hartmut Kohli, Pushmeet Quantum Physics Machine Learning 81P73, 68T07 I.2.0; J.2 Quantum error-correction is a prerequisite for reliable quantum computation. Towards this goal, we present a recurrent, transformer-based neural network which learns to decode the surface code, the leading quantum error-correction code. Our decoder outperforms state-of-the-art algorithmic decoders on real-world data from Google's Sycamore quantum processor for distance 3 and 5 surface codes. On distances up to 11, the decoder maintains its advantage on simulated data with realistic noise including cross-talk, leakage, and analog readout signals, and sustains its accuracy far beyond the 25 cycles it was trained on. Our work illustrates the ability of machine learning to go beyond human-designed algorithms by learning from data directly, highlighting machine learning as a strong contender for decoding in quantum computers. |
| title | Learning to Decode the Surface Code with a Recurrent, Transformer-Based Neural Network |
| topic | Quantum Physics Machine Learning 81P73, 68T07 I.2.0; J.2 |
| url | https://arxiv.org/abs/2310.05900 |