PNNL

Abstract

In recent years the prospects of quantum machine learning, as exemplified by quantum deep neural networks, have gain notoriety in the scientific community. By combining ideas from quantum computing with machine learning methodology, these models promise new ways to interpret classical and quantum data sets. In our work, we connect quantum thermodynamics and quantum deep learning to demonstrate that, with high probability, entanglement between the visible and hidden units will lead to states that are exponentially close to a maximally mixed state. Moreover, the gradients of such models with respect to their parameters will vanish as the dimension of the system increases for reasonable random ensembles of quantum models. We call this effect entanglement induced barren plateaus and argue that their existence necessitates a re-evaluation of how we think about the role that entanglement and other quantum effects play within quantum machine learning algorithms.