Opportunities and limitations of explaining quantum machine learning
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866917874082250752 |
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| author | Gil-Fuster, Elies Naujoks, Jonas R. Montavon, Grégoire Wiegand, Thomas Samek, Wojciech Eisert, Jens |
| author_facet | Gil-Fuster, Elies Naujoks, Jonas R. Montavon, Grégoire Wiegand, Thomas Samek, Wojciech Eisert, Jens |
| contents | A common trait of many machine learning models is that it is often difficult to understand and explain what caused the model to produce the given output. While the explainability of neural networks has been an active field of research in the last years, comparably little is known for quantum machine learning models. Despite a few recent works analyzing some specific aspects of explainability, as of now there is no clear big picture perspective as to what can be expected from quantum learning models in terms of explainability. In this work, we address this issue by identifying promising research avenues in this direction and lining out the expected future results. We additionally propose two explanation methods designed specifically for quantum machine learning models, as first of their kind to the best of our knowledge. Next to our pre-view of the field, we compare both existing and novel methods to explain the predictions of quantum learning models. By studying explainability in quantum machine learning, we can contribute to the sustainable development of the field, preventing trust issues in the future. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_14753 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Opportunities and limitations of explaining quantum machine learning Gil-Fuster, Elies Naujoks, Jonas R. Montavon, Grégoire Wiegand, Thomas Samek, Wojciech Eisert, Jens Quantum Physics Machine Learning A common trait of many machine learning models is that it is often difficult to understand and explain what caused the model to produce the given output. While the explainability of neural networks has been an active field of research in the last years, comparably little is known for quantum machine learning models. Despite a few recent works analyzing some specific aspects of explainability, as of now there is no clear big picture perspective as to what can be expected from quantum learning models in terms of explainability. In this work, we address this issue by identifying promising research avenues in this direction and lining out the expected future results. We additionally propose two explanation methods designed specifically for quantum machine learning models, as first of their kind to the best of our knowledge. Next to our pre-view of the field, we compare both existing and novel methods to explain the predictions of quantum learning models. By studying explainability in quantum machine learning, we can contribute to the sustainable development of the field, preventing trust issues in the future. |
| title | Opportunities and limitations of explaining quantum machine learning |
| topic | Quantum Physics Machine Learning |
| url | https://arxiv.org/abs/2412.14753 |