Deep learning optimal quantum annealing schedules for random Ising models
A crucial step in the race towards quantum advantage is optimizing quantum annealing using ad-hoc annealing schedules. Motivated by recent progress in the field, we propose to employ long-short term memory neural networks to automate the search for optimal annealing schedules for random Ising models...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2023-01-01
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| Series: | New Journal of Physics |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/1367-2630/ace547 |
| _version_ | 1797748684820054016 |
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| author | Pratibha Raghupati Hegde Gianluca Passarelli Giovanni Cantele Procolo Lucignano |
| author_facet | Pratibha Raghupati Hegde Gianluca Passarelli Giovanni Cantele Procolo Lucignano |
| author_sort | Pratibha Raghupati Hegde |
| collection | DOAJ |
| description | A crucial step in the race towards quantum advantage is optimizing quantum annealing using ad-hoc annealing schedules. Motivated by recent progress in the field, we propose to employ long-short term memory neural networks to automate the search for optimal annealing schedules for random Ising models on regular graphs. By training our network using locally-adiabatic annealing paths, we are able to predict optimal annealing schedules for unseen instances and even larger graphs than those used for training. |
| first_indexed | 2024-03-12T16:08:19Z |
| format | Article |
| id | doaj.art-417cc72cef5e4c3b928cfd5afce7b4af |
| institution | Directory Open Access Journal |
| issn | 1367-2630 |
| language | English |
| last_indexed | 2024-03-12T16:08:19Z |
| publishDate | 2023-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | New Journal of Physics |
| spelling | doaj.art-417cc72cef5e4c3b928cfd5afce7b4af2023-08-09T14:14:34ZengIOP PublishingNew Journal of Physics1367-26302023-01-0125707301310.1088/1367-2630/ace547Deep learning optimal quantum annealing schedules for random Ising modelsPratibha Raghupati Hegde0https://orcid.org/0000-0002-6059-8249Gianluca Passarelli1https://orcid.org/0000-0002-3292-0034Giovanni Cantele2https://orcid.org/0000-0002-9567-3536Procolo Lucignano3https://orcid.org/0000-0003-2784-8485Dipartimento di Fisica ‘E. Pancini’, Università degli Studi di Napoli ‘Federico II’, Complesso Universitario M. S. Angelo , via Cintia 21, 80126 Napoli, ItalyCNR-SPIN, c/o Complesso Universitario M. S. Angelo , via Cintia 21, 80126 Napoli, ItalyCNR-SPIN, c/o Complesso Universitario M. S. Angelo , via Cintia 21, 80126 Napoli, ItalyDipartimento di Fisica ‘E. Pancini’, Università degli Studi di Napoli ‘Federico II’, Complesso Universitario M. S. Angelo , via Cintia 21, 80126 Napoli, ItalyA crucial step in the race towards quantum advantage is optimizing quantum annealing using ad-hoc annealing schedules. Motivated by recent progress in the field, we propose to employ long-short term memory neural networks to automate the search for optimal annealing schedules for random Ising models on regular graphs. By training our network using locally-adiabatic annealing paths, we are able to predict optimal annealing schedules for unseen instances and even larger graphs than those used for training.https://doi.org/10.1088/1367-2630/ace547quantum annealingmachine learningquantum optimisationadiabatic quantum algorithms |
| spellingShingle | Pratibha Raghupati Hegde Gianluca Passarelli Giovanni Cantele Procolo Lucignano Deep learning optimal quantum annealing schedules for random Ising models New Journal of Physics quantum annealing machine learning quantum optimisation adiabatic quantum algorithms |
| title | Deep learning optimal quantum annealing schedules for random Ising models |
| title_full | Deep learning optimal quantum annealing schedules for random Ising models |
| title_fullStr | Deep learning optimal quantum annealing schedules for random Ising models |
| title_full_unstemmed | Deep learning optimal quantum annealing schedules for random Ising models |
| title_short | Deep learning optimal quantum annealing schedules for random Ising models |
| title_sort | deep learning optimal quantum annealing schedules for random ising models |
| topic | quantum annealing machine learning quantum optimisation adiabatic quantum algorithms |
| url | https://doi.org/10.1088/1367-2630/ace547 |
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