Digitized Counterdiabatic Quantum Feature Extraction

• URL: http://arxiv.org/abs/2510.13807v1
• Date: Wed, 15 Oct 2025 17:59:35 GMT
• Title: Digitized Counterdiabatic Quantum Feature Extraction
• Authors: Anton Simen, Carlos Flores-Garrigós, Murilo Henrique De Oliveira, Gabriel Dario Alvarado Barrios, Alejandro Gomez Cadavid, Archismita Dalal, Enrique Solano, Narendra N. Hegade, Qi Zhang,
• Abstract summary: We introduce a Hamiltonian-based quantum feature extraction method that generates complex features via the dynamics of $k$-local many-body spins Hamiltonians.<n>We assess the approach on high-dimensional, real-world datasets, including molecular toxicity classification and image recognition.<n>The results suggest that combining quantum and classical feature extraction can provide consistent improvements across diverse machine learning tasks.
• Score: 35.670314643295036
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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