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 …

Tensor networks (TNs) have become one of the most essential building blocks for various
fields of theoretical physics such as condensed matter theory, statistical mechanics …

ITensor is a system for programming tensor network calculations with an interface modeled
on tensor diagram notation, which allows users to focus on the connectivity of a tensor …

Calculating perturbation response properties of materials from first principles provides a vital
link between theory and experiment, but is bottlenecked by the high computational cost …

Including prior knowledge is important for effective machine learning models in physics and
is usually achieved by explicitly adding loss terms or constraints on model architectures …

In this book, we provide a comprehensive introduction to the most recent advances in the
application of machine learning methods in quantum sciences. We cover the use of deep …

The power of quantum chemistry to predict the ground and excited state properties of
complex chemical systems has driven the development of computational quantum chemistry …

Tensor networks (TNs) are a family of computational methods built on graph-structured
factorizations of large tensors, which have long represented state-of-the-art methods for the …

Hamiltonian simulation is a promising application for quantum computers to achieve a
quantum advantage. We present classical algorithms based on tensor network methods to …

Quantum state preparation is a vital routine in many quantum algorithms, including solution
of linear systems of equations, Monte Carlo simulations, quantum sampling, and machine …