A Resource Efficient Quantum Kernel

• URL: http://arxiv.org/abs/2507.03689v2
• Date: Tue, 15 Jul 2025 15:37:48 GMT
• Title: A Resource Efficient Quantum Kernel
• Authors: Utkarsh Singh, Jean-Frédéric Laprade, Aaron Z. Goldberg, Khabat Heshami,
• Abstract summary: We introduce a quantum kernel designed to handle high-dimensional data with a significantly reduced number of qubits and entangling operations.<n>Our approach preserves essential data characteristics while promoting computational efficiency.<n>Our findings herald a promising avenue for the practical implementation of quantum machine learning algorithms on near future quantum computing platforms.
• Score: 1.7249361224827533
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantum processors may enhance machine learning by mapping high-dimensional data onto quantum systems for processing. Conventional quantum kernels, or feature maps, for encoding data features 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 kernel 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, as compared to state-of-the-art quantum feature maps. Our noisy simulations results combined with lower resource requirements highlight our kernel's ability to function within the constraints of noisy intermediate-scale quantum devices. Through numerical simulations and small-scale implementation on a superconducting circuit quantum computing platform, we demonstrate that our scheme performs on par or better than a set of classical algorithms for classification. Our findings herald a promising avenue for the practical implementation of quantum machine learning algorithms on near future quantum computing platforms.

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