Laziness, Barren Plateau, and Noise in Machine Learning

Understand

We define \emph{laziness} to describe a large suppression of variational parameter updates for neural networks, classical or quantum.

• In the quantum case, the suppression is exponential in the number of qubits for randomized variational quantum circuits.
• We discuss the difference between laziness and \emph{barren plateau} in quantum machine learning created by quantum physicists in \cite{mcclean2018barren} for the flatness of the loss function landscape during gradient descent.
• We address a novel theoretical understanding of those two phenomena in light of the theory of neural tangent kernels.