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Importance of Kernel Bandwidth in Quantum Machine Learning
Quantum kernel methods are considered a promising avenue for applying quantum computers to machine learning problems. However, recent results overlook the central role hyperparameters play in determining the performance of machine learning methods. In this work we identify the hyperparameter controlling the bandwidth of a quantum kernel and show that it controls the expressivity of the resulting model. We use extensive numerical experiments with multiple quantum kernels and classical datasetsdoi:10.21203/rs.3.rs-1087188/v1 fatcat:im3tbjj4gbeavajyes6mz33lyu