[2603.19805] Quantifying Gate Contribution in Quantum Feature Maps for Scalable Circuit Optimization

Computer Science > Machine Learning arXiv:2603.19805 (cs) [Submitted on 20 Mar 2026] Title:Quantifying Gate Contribution in Quantum Feature Maps for Scalable Circuit Optimization Authors:F. Rodríguez-Díaz, D. Gutiérrez-Avilés, A. Troncoso, F. Martínez-Álvarez View a PDF of the paper titled Quantifying Gate Contribution in Quantum Feature Maps for Scalable Circuit Optimization, by F. Rodr\'iguez-D\'iaz and 3 other authors View PDF Abstract:Quantum machine learning offers promising advantages for classification tasks, but noise, decoherence, and connectivity constraints in current devices continue to limit the efficient execution of feature map-based circuits. Gate Assessment and Threshold Evaluation (GATE) is presented as a circuit optimization methodology that reduces quantum feature maps using a novel gate significance index. This index quantifies the relevance of each gate by combining fidelity, entanglement, and sensitivity. It is formulated for both simulator/emulator environments, where quantum states are accessible, and for real hardware, where these quantities are estimated from measurement results and auxiliary circuits. The approach iteratively scans a threshold range, eliminates low-contribution gates, generates optimized quantum machine learning models, and ranks them based on accuracy, runtime, and a balanced performance criterion before final testing. The methodology is evaluated on real-world classification datasets using two representative quantum machine le...