On a quantum inspired approach to train machine learning models
Abstract In this work, a novel technique to train machine learning models is introduced, which is based on digital simulations of certain types of quantum systems. This represents a drastic departure from the standard approach of quantum machine learning which, to this day, is based on the use of ac...
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| Format: | Article |
| Language: | English |
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Wiley
2023-12-01
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| Series: | Applied AI Letters |
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| Online Access: | https://doi.org/10.1002/ail2.89 |
| _version_ | 1797369689365544960 |
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| author | Jean Michel Sellier |
| author_facet | Jean Michel Sellier |
| author_sort | Jean Michel Sellier |
| collection | DOAJ |
| description | Abstract In this work, a novel technique to train machine learning models is introduced, which is based on digital simulations of certain types of quantum systems. This represents a drastic departure from the standard approach of quantum machine learning which, to this day, is based on the use of actual physical quantum systems. To provide a clear context, the field of quantum inspired machine learning is first provided. Then, we proceed with a detailed description of our proposed method. To conclude, some preliminary, yet compelling, results are presented and discussed. Although at a seminal stage, the author firmly believes that this approach could represent a valid and robust alternative to the way machine learning models are trained today. |
| first_indexed | 2024-03-08T17:50:28Z |
| format | Article |
| id | doaj.art-d38fd5fb9e5b48a8852aeb29de42c3bf |
| institution | Directory Open Access Journal |
| issn | 2689-5595 |
| language | English |
| last_indexed | 2024-03-08T17:50:28Z |
| publishDate | 2023-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied AI Letters |
| spelling | doaj.art-d38fd5fb9e5b48a8852aeb29de42c3bf2024-01-02T08:29:25ZengWileyApplied AI Letters2689-55952023-12-0144n/an/a10.1002/ail2.89On a quantum inspired approach to train machine learning modelsJean Michel Sellier0Global AI Accelerator Ericsson Montréal Québec CanadaAbstract In this work, a novel technique to train machine learning models is introduced, which is based on digital simulations of certain types of quantum systems. This represents a drastic departure from the standard approach of quantum machine learning which, to this day, is based on the use of actual physical quantum systems. To provide a clear context, the field of quantum inspired machine learning is first provided. Then, we proceed with a detailed description of our proposed method. To conclude, some preliminary, yet compelling, results are presented and discussed. Although at a seminal stage, the author firmly believes that this approach could represent a valid and robust alternative to the way machine learning models are trained today.https://doi.org/10.1002/ail2.89artificial neural networksmachine learningoptimization problemsquantum computingquantum inspired methodsquantum machine learning |
| spellingShingle | Jean Michel Sellier On a quantum inspired approach to train machine learning models Applied AI Letters artificial neural networks machine learning optimization problems quantum computing quantum inspired methods quantum machine learning |
| title | On a quantum inspired approach to train machine learning models |
| title_full | On a quantum inspired approach to train machine learning models |
| title_fullStr | On a quantum inspired approach to train machine learning models |
| title_full_unstemmed | On a quantum inspired approach to train machine learning models |
| title_short | On a quantum inspired approach to train machine learning models |
| title_sort | on a quantum inspired approach to train machine learning models |
| topic | artificial neural networks machine learning optimization problems quantum computing quantum inspired methods quantum machine learning |
| url | https://doi.org/10.1002/ail2.89 |
| work_keys_str_mv | AT jeanmichelsellier onaquantuminspiredapproachtotrainmachinelearningmodels |