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 …

Artificial intelligence (AI) and classical machine learning (ML) techniques have
revolutionised numerous fields, including quantum communication. Quantum …

Renormalization group theory of tensor network states provides a powerful tool for studying
quantum many-body problems and a new paradigm for understanding entangled structures …

In recent advancements in agricultural technology, quantum mechanics and deep learning
integration have shown promising potential to revolutionize rice yield forecasting methods …

Quantum entanglement is one of the essential resources involved in quantum information
processing tasks. However, its detection for usage remains a challenge. The Bell-type …

Healthcare industries frequently handle sensitive and proprietary data, and due to strict
privacy regulations, they are often reluctant to share data directly. In today's context …

We present an overview of the key ideas and skills necessary to begin implementing tensor
network methods numerically, which is intended to facilitate the practical application of …

Tensor networks developed in the context of condensed matter physics try to approximate
order-tensors with a reduced number of degrees of freedom that is only polynomial in and …

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 …

Ascertaining whether a classical model can efficiently replace a given quantum model—
dequantization—is crucial in assessing the true potential of quantum algorithms. In this work …