Prospects for quantum advantage in machine learning from the representability of functions
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866911331175628800 |
|---|---|
| author | Masot-Llima, Sergi Gil-Fuster, Elies Bravo-Prieto, Carlos Eisert, Jens Guaita, Tommaso |
| author_facet | Masot-Llima, Sergi Gil-Fuster, Elies Bravo-Prieto, Carlos Eisert, Jens Guaita, Tommaso |
| contents | Demonstrating quantum advantage in machine learning tasks requires navigating a complex landscape of proposed models and algorithms. To bring clarity to this search, we introduce a framework that connects the structure of parametrized quantum circuits to the mathematical nature of the functions they can actually learn. Within this framework, we show how fundamental properties, like circuit depth and non-Clifford gate count, directly determine whether a model's output leads to efficient classical simulation or surrogation. We argue that this analysis uncovers common pathways to dequantization that underlie many existing simulation methods. More importantly, it reveals critical distinctions between models that are fully simulatable, those whose function space is classically tractable, and those that remain robustly quantum. This perspective provides a conceptual map of this landscape, clarifying how different models relate to classical simulability and pointing to where opportunities for quantum advantage may lie. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_15661 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Prospects for quantum advantage in machine learning from the representability of functions Masot-Llima, Sergi Gil-Fuster, Elies Bravo-Prieto, Carlos Eisert, Jens Guaita, Tommaso Quantum Physics Machine Learning Demonstrating quantum advantage in machine learning tasks requires navigating a complex landscape of proposed models and algorithms. To bring clarity to this search, we introduce a framework that connects the structure of parametrized quantum circuits to the mathematical nature of the functions they can actually learn. Within this framework, we show how fundamental properties, like circuit depth and non-Clifford gate count, directly determine whether a model's output leads to efficient classical simulation or surrogation. We argue that this analysis uncovers common pathways to dequantization that underlie many existing simulation methods. More importantly, it reveals critical distinctions between models that are fully simulatable, those whose function space is classically tractable, and those that remain robustly quantum. This perspective provides a conceptual map of this landscape, clarifying how different models relate to classical simulability and pointing to where opportunities for quantum advantage may lie. |
| title | Prospects for quantum advantage in machine learning from the representability of functions |
| topic | Quantum Physics Machine Learning |
| url | https://arxiv.org/abs/2512.15661 |