Digitized Counterdiabatic Quantum Feature Extraction
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915556189274112 |
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| author | Simen, Anton Flores-Garrigós, Carlos De Oliveira, Murilo Henrique Barrios, Gabriel Dario Alvarado Cadavid, Alejandro Gomez Dalal, Archismita Solano, Enrique Hegade, Narendra N. Zhang, Qi |
| author_facet | Simen, Anton Flores-Garrigós, Carlos De Oliveira, Murilo Henrique Barrios, Gabriel Dario Alvarado Cadavid, Alejandro Gomez Dalal, Archismita Solano, Enrique Hegade, Narendra N. Zhang, Qi |
| contents | We introduce a Hamiltonian-based quantum feature extraction method that generates complex features via the dynamics of $k$-local many-body spins Hamiltonians, enhancing machine learning performance. Classical feature vectors are embedded into spin-glass Hamiltonians, where both single-variable contributions and higher-order correlations are represented through many-body interactions. By evolving the system under suitable quantum dynamics on IBM digital quantum processors with 156 qubits, the data are mapped into a higher-dimensional feature space via expectation values of low- and higher-order observables. This allows us to capture statistical dependencies that are difficult to access with standard classical methods. We assess the approach on high-dimensional, real-world datasets, including molecular toxicity classification and image recognition, and analyze feature importance to show that quantum-extracted features complement and, in many cases, surpass classical ones. The results suggest that combining quantum and classical feature extraction can provide consistent improvements across diverse machine learning tasks, indicating a reliable level of early quantum usefulness for near-term quantum devices in data-driven applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_13807 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Digitized Counterdiabatic Quantum Feature Extraction Simen, Anton Flores-Garrigós, Carlos De Oliveira, Murilo Henrique Barrios, Gabriel Dario Alvarado Cadavid, Alejandro Gomez Dalal, Archismita Solano, Enrique Hegade, Narendra N. Zhang, Qi Quantum Physics We introduce a Hamiltonian-based quantum feature extraction method that generates complex features via the dynamics of $k$-local many-body spins Hamiltonians, enhancing machine learning performance. Classical feature vectors are embedded into spin-glass Hamiltonians, where both single-variable contributions and higher-order correlations are represented through many-body interactions. By evolving the system under suitable quantum dynamics on IBM digital quantum processors with 156 qubits, the data are mapped into a higher-dimensional feature space via expectation values of low- and higher-order observables. This allows us to capture statistical dependencies that are difficult to access with standard classical methods. We assess the approach on high-dimensional, real-world datasets, including molecular toxicity classification and image recognition, and analyze feature importance to show that quantum-extracted features complement and, in many cases, surpass classical ones. The results suggest that combining quantum and classical feature extraction can provide consistent improvements across diverse machine learning tasks, indicating a reliable level of early quantum usefulness for near-term quantum devices in data-driven applications. |
| title | Digitized Counterdiabatic Quantum Feature Extraction |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2510.13807 |