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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.20975 |
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| _version_ | 1866908508699492352 |
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| author | Simen, Anton Flores-Garrigos, Carlos De Oliveira, Murilo Henrique Barrios, Gabriel Dario Alvarado Hernández, Juan F. R. Zhang, Qi Cadavid, Alejandro Gomez Vives-Gilabert, Yolanda Martín-Guerrero, José D. Solano, Enrique Hegade, Narendra N. Dalal, Archismita |
| author_facet | Simen, Anton Flores-Garrigos, Carlos De Oliveira, Murilo Henrique Barrios, Gabriel Dario Alvarado Hernández, Juan F. R. Zhang, Qi Cadavid, Alejandro Gomez Vives-Gilabert, Yolanda Martín-Guerrero, José D. Solano, Enrique Hegade, Narendra N. Dalal, Archismita |
| contents | We propose a quantum feature mapping technique that leverages the quench dynamics of a quantum spin glass to extract complex data patterns at the quantum-advantage level for academic and industrial applications. We demonstrate that encoding a dataset information into disordered quantum many-body spin-glass problems, followed by a nonadiabatic evolution and feature extraction via measurements of expectation values, significantly enhances machine learning (ML) models. By analyzing the performance of our protocol over a range of evolution times, we empirically show that ML models benefit most from feature representations obtained in the fast coherent regime of a quantum annealer, particularly near the critical point of the quantum dynamics. We demonstrate the generalization of our technique by benchmarking on multiple high-dimensional datasets, involving over a hundred features, in applications including drug discovery and medical diagnostics. Moreover, we compare against a comprehensive suite of state-of-the-art classical ML models and show that our quantum feature maps can enhance the performance metrics of the baseline classical models up to 210%. Our work presents the first quantum ML demonstrations at the quantum-advantage level, bridging the gap between quantum supremacy and useful real-world academic and industrial applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_20975 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quenched Quantum Feature Maps Simen, Anton Flores-Garrigos, Carlos De Oliveira, Murilo Henrique Barrios, Gabriel Dario Alvarado Hernández, Juan F. R. Zhang, Qi Cadavid, Alejandro Gomez Vives-Gilabert, Yolanda Martín-Guerrero, José D. Solano, Enrique Hegade, Narendra N. Dalal, Archismita Quantum Physics We propose a quantum feature mapping technique that leverages the quench dynamics of a quantum spin glass to extract complex data patterns at the quantum-advantage level for academic and industrial applications. We demonstrate that encoding a dataset information into disordered quantum many-body spin-glass problems, followed by a nonadiabatic evolution and feature extraction via measurements of expectation values, significantly enhances machine learning (ML) models. By analyzing the performance of our protocol over a range of evolution times, we empirically show that ML models benefit most from feature representations obtained in the fast coherent regime of a quantum annealer, particularly near the critical point of the quantum dynamics. We demonstrate the generalization of our technique by benchmarking on multiple high-dimensional datasets, involving over a hundred features, in applications including drug discovery and medical diagnostics. Moreover, we compare against a comprehensive suite of state-of-the-art classical ML models and show that our quantum feature maps can enhance the performance metrics of the baseline classical models up to 210%. Our work presents the first quantum ML demonstrations at the quantum-advantage level, bridging the gap between quantum supremacy and useful real-world academic and industrial applications. |
| title | Quenched Quantum Feature Maps |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2508.20975 |