Digitized Counterdiabatic Quantum Feature Extraction

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.