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QUACK: Quantum Aligned Centroid Kernel
May 2, 2024, 4:42 a.m. | Kilian Tscharke, Sebastian Issel, Pascal Debus
cs.LG updates on arXiv.org arxiv.org
Abstract: Quantum computing (QC) seems to show potential for application in machine learning (ML). In particular quantum kernel methods (QKM) exhibit promising properties for use in supervised ML tasks. However, a major disadvantage of kernel methods is their unfavorable quadratic scaling with the number of training samples. Together with the limits imposed by currently available quantum hardware (NISQ devices) with their low qubit coherence times, small number of qubits, and high error rates, the use of …
abstract application arxiv computing cs.lg however kernel machine machine learning major quant-ph quantum quantum computing samples scaling show tasks together training type
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