Kernel density estimation (KDE) is a versatile nonparametric approach to infer continuous probability distributions from finite samples. By superimposing smooth kernel functions—most commonly Gaussian ...

The functional characterization of different neuronal types has been a longstanding and crucial challenge. With the advent of physical quantum computers, it has become possible to apply quantum ...