Importance of Kernel Bandwidth in Quantum Machine Learning. (arXiv:2111.05451v3 [quant-ph] UPDATED)

Web: http://arxiv.org/abs/2111.05451

Quantum kernel methods are considered a promising avenue for applying quantum
computers to machine learning problems. Identifying hyperparameters controlling
the inductive bias of quantum machine learning models is expected to be crucial
given the central role hyperparameters play in determining the performance of
classical machine learning methods. In this work we introduce 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 …

arxiv kernel learning machine machine learning quantum