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 …

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 …

Once developed for quantum theory, tensor networks (TNs) have been established as a
successful machine learning (ML) paradigm. Now, they have been ported back to the …

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 …

We perform quantum process tomography (QPT) for both discrete-and continuous-variable
quantum systems by learning a process representation using Kraus operators. The Kraus …

Several tensor networks are built of isometric tensors, ie tensors satisfying $ W^\dagger
W=\mathrm {I} $. Prominent examples include matrix product states (MPS) in canonical form …

Variational quantum algorithms are a promising class of algorithms that can be performed
on currently available quantum computers. In most settings, the free parameters of a …

We investigate the computational power of the recently introduced class of isometric tensor
network states (isoTNSs), which generalizes the isometric conditions of the canonical form of …

Abstract Machine learning tasks are an exciting application for quantum computers, as it has
been proven that they can learn certain problems more efficiently than classical ones …

Running quantum algorithms often involves implementing complex quantum circuits with
such a large number of multi-qubit gates that the challenge of tackling practical applications …