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

Tensor networks provide extremely powerful tools for the study of complex classical and
quantum many-body problems. Over the past two decades, the increment in the number of …

Neural network-based machine learning has recently proven successful for many complex
applications ranging from image recognition to precision medicine. However, its direct …

Modern applications in engineering and data science are increasingly based on
multidimensional data of exceedingly high volume, variety, and structural richness …

A bstract We propose a family of exactly solvable toy models for the AdS/CFT
correspondence based on a novel construction of quantum error-correcting codes with a …

This is a partly non-technical introduction to selected topics on tensor network methods,
based on several lectures and introductory seminars given on the subject. It should be a …

The curse of dimensionality associated with the Hilbert space of spin systems provides a
significant obstruction to the study of condensed matter systems. Tensor networks have …

We propose a general method to embed target states into the middle of the energy spectrum
of a many-body Hamiltonian as its energy eigenstates. Employing this method, we construct …

Quantum computers, though not yet available on the market, will revolutionize the future of
information processing. Quantum computers for special purposes like quantum simulators …

The restricted Boltzmann machine (RBM) is one of the fundamental building blocks of deep
learning. RBM finds wide applications in dimensional reduction, feature extraction, and …