The theory of entanglement provides a fundamentally new language for describing
interactions and correlations in many-body systems. Its vocabulary consists of qubits and …

Originally developed in the context of condensed-matter physics and based on
renormalization group ideas, tensor networks have been revived thanks to quantum …

The accurate computation of Hamiltonian ground, excited and thermal states on quantum
computers stands to impact many problems in the physical and computer sciences, from …

Quantum random sampling is the leading proposal for demonstrating a computational
advantage of quantum computers over classical computers. Recently the first large-scale …

Coupling a quantum many-body system to an external environment dramatically changes its
dynamics and offers novel possibilities not found in closed systems. Of special interest are …

Gauge theories are of paramount importance in our understanding of fundamental
constituents of matter and their interactions. However, the complete characterization of their …

We establish a direct connection between general tensor networks and deep feed-forward
artificial neural networks. The core of our results is the construction of neural-network layers …

We develop a variational approach to simulating the dynamics of open quantum many-body
systems using deep autoregressive neural networks. The parameters of a compressed …

We introduce a novel tensor network structure augmenting the well-established tree tensor
network representation of a quantum many-body wave function. The new structure satisfies …

The thermal or equilibrium ensemble is one of the most ubiquitous states of matter. For
models comprised of many locally interacting quantum particles, it describes a wide range of …