Hamiltonian learning for quantum error correction
The efficient validation of quantum devices is critical for emerging technological applications. In a wide class of use cases the precise engineering of a Hamiltonian is required both for the implementation of gate-based quantum information processing as well as for reliable quantum memories. Inferr...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
American Physical Society
2019-11-01
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| Series: | Physical Review Research |
| Online Access: | http://doi.org/10.1103/PhysRevResearch.1.033092 |
| _version_ | 1797211651005480960 |
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| author | Agnes Valenti Evert van Nieuwenburg Sebastian Huber Eliska Greplova |
| author_facet | Agnes Valenti Evert van Nieuwenburg Sebastian Huber Eliska Greplova |
| author_sort | Agnes Valenti |
| collection | DOAJ |
| description | The efficient validation of quantum devices is critical for emerging technological applications. In a wide class of use cases the precise engineering of a Hamiltonian is required both for the implementation of gate-based quantum information processing as well as for reliable quantum memories. Inferring the experimentally realized Hamiltonian through a scalable number of measurements constitutes the challenging task of Hamiltonian learning. In particular, assessing the quality of the implementation of topological codes is essential for quantum error correction. Here, we introduce a neural-net-based approach to this challenge. We capitalize on a family of exactly solvable models to train our algorithm and generalize to a broad class of experimentally relevant sources of errors. We discuss how our algorithm scales with system size and analyze its resilience toward various noise sources. |
| first_indexed | 2024-04-24T10:29:52Z |
| format | Article |
| id | doaj.art-0ffa4b592cb34d5fa5993a92171de883 |
| institution | Directory Open Access Journal |
| issn | 2643-1564 |
| language | English |
| last_indexed | 2024-04-24T10:29:52Z |
| publishDate | 2019-11-01 |
| publisher | American Physical Society |
| record_format | Article |
| series | Physical Review Research |
| spelling | doaj.art-0ffa4b592cb34d5fa5993a92171de8832024-04-12T16:46:50ZengAmerican Physical SocietyPhysical Review Research2643-15642019-11-011303309210.1103/PhysRevResearch.1.033092Hamiltonian learning for quantum error correctionAgnes ValentiEvert van NieuwenburgSebastian HuberEliska GreplovaThe efficient validation of quantum devices is critical for emerging technological applications. In a wide class of use cases the precise engineering of a Hamiltonian is required both for the implementation of gate-based quantum information processing as well as for reliable quantum memories. Inferring the experimentally realized Hamiltonian through a scalable number of measurements constitutes the challenging task of Hamiltonian learning. In particular, assessing the quality of the implementation of topological codes is essential for quantum error correction. Here, we introduce a neural-net-based approach to this challenge. We capitalize on a family of exactly solvable models to train our algorithm and generalize to a broad class of experimentally relevant sources of errors. We discuss how our algorithm scales with system size and analyze its resilience toward various noise sources.http://doi.org/10.1103/PhysRevResearch.1.033092 |
| spellingShingle | Agnes Valenti Evert van Nieuwenburg Sebastian Huber Eliska Greplova Hamiltonian learning for quantum error correction Physical Review Research |
| title | Hamiltonian learning for quantum error correction |
| title_full | Hamiltonian learning for quantum error correction |
| title_fullStr | Hamiltonian learning for quantum error correction |
| title_full_unstemmed | Hamiltonian learning for quantum error correction |
| title_short | Hamiltonian learning for quantum error correction |
| title_sort | hamiltonian learning for quantum error correction |
| url | http://doi.org/10.1103/PhysRevResearch.1.033092 |
| work_keys_str_mv | AT agnesvalenti hamiltonianlearningforquantumerrorcorrection AT evertvannieuwenburg hamiltonianlearningforquantumerrorcorrection AT sebastianhuber hamiltonianlearningforquantumerrorcorrection AT eliskagreplova hamiltonianlearningforquantumerrorcorrection |