Efficient Parameter Optimisation for Quantum Kernel Alignment: A Sub-sampling Approach in Variational Training
Published in arXiv (Cornell University) • Jan 5, 2024
NobleIDNI3P39W92R01S54
Authors:,,
Mehmet Şahin
Benjamin C. B. Symons
Pushpak Pati
Abstract
Quantum machine learning with quantum kernels for classification problems is a growing area of research. Recently, quantum kernel alignment techniques that parameterise the kernel have been developed, allowing the kernel to be trained and therefore aligned with a specific dataset. While quantum kern...
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