It is a critical challenge to simultaneously achieve high interpretability and high efficiency
with the current schemes of deep machine learning (ML). The tensor network (TN), a well …

Solving for the lowest energy eigenstate of the many-body Schrodinger equation is a
cornerstone problem that hinders understanding of a variety of quantum phenomena. The …

The search for novel entangled phases of matter has lead to the recent discovery of a new
class of “entanglement transitions,” exemplified by random tensor networks and monitored …

A bstract The generalized quantum double lattice realization of 2d topological orders based
on Hopf algebras is discussed in this work. Both left-module and right-module constructions …

The question of what makes a data distribution suitable for deep learning is a fundamental
open problem. Focusing on locally connected neural networks (a prevalent family of …

Abstract Machine learning has shown significant breakthroughs in quantum science, where
in particular deep neural networks exhibited remarkable power in modeling quantum many …

Tiny object classification problem exists in many machine learning applications like medical
imaging or remote sensing, where the object of interest usually occupies a small region of …

We discuss and demonstrate an unsupervised machine-learning procedure to detect
topological order in quantum many-body systems. Using a restricted Boltzmann machine to …

The complete learning of an $ n $-qubit quantum state requires samples exponentially in $ n
$. Several works consider subclasses of quantum states that can be learned in polynomial …

Traditional machine-learning methods are inefficient in capturing chaos in nonlinear
dynamical systems, especially when the time difference Δ t between consecutive steps is so …