AI RESEARCH
Quantifying Gate Contribution in Quantum Feature Maps for Scalable Circuit Optimization
arXiv CS.LG
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ArXi:2603.19805v1 Announce Type: new 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.