Fig. 1: Illustration of setup and main results of this work.
From: Dynamical transition in controllable quantum neural networks with large depth
We study the training dynamics of quantum neural networks with loss function \({{{\mathcal{L}}}}({{{\boldsymbol{\theta }}}})={(\langle \hat{O}\rangle -{O}_{0})}^{2}/2\), and identify a dynamical transition. We derive a first-principle generalized Lotka-Volterra model to characterize it, and also provide interpretations from random unitary ensemble and Schrödinger equation.
