[2507.03689] A Resource Efficient Quantum Kernel

Quantum Physics arXiv:2507.03689 (quant-ph) [Submitted on 4 Jul 2025 (v1), last revised 26 Mar 2026 (this version, v4)] Title:A Resource Efficient Quantum Kernel Authors:Utkarsh Singh, Jean-Frédéric Laprade, Aaron Z. Goldberg, Khabat Heshami View a PDF of the paper titled A Resource Efficient Quantum Kernel, by Utkarsh Singh and 3 other authors View PDF HTML (experimental) Abstract:Quantum processors may enhance machine learning by mapping high-dimensional data onto quantum systems for processing. Conventional feature maps, for encoding data onto a quantum circuit are currently impractical, as the number of entangling gates scales quadratically with the dimension of the dataset and the number of qubits. In this work, we introduce a quantum feature map designed to handle high-dimensional data with a significantly reduced number of qubits and entangling operations. Our approach preserves essential data characteristics while promoting computational efficiency, as evidenced by extensive experiments on benchmark datasets that demonstrate a marked improvement in both accuracy and resource utilization when using our feature map as a kernel for characterization, as compared to state-of-the-art quantum feature maps. Our noisy simulation results, combined with lower resource requirements, highlight our map's ability to function within the constraints of noisy intermediate-scale quantum devices. Through numerical simulations and small-scale implementation on a superconducting circuit...