Abstract Machine Learning (ML) is one of the most exciting and dynamic areas of modern
research and application. The purpose of this review is to provide an introduction to the core …

The power of quantum computing technologies is based on the fundamentals of quantum
mechanics, such as quantum superposition, quantum entanglement, or the no-cloning …

Quantum information technologies, on the one hand, and intelligent learning systems, on the
other, are both emergent technologies that are likely to have a transformative impact on our …

Quantum classification and hypothesis testing (state and channel discrimination) are two
tightly related subjects, the main difference being that the former is data driven: how to …

Adversarial machine learning is an emerging field that focuses on studying vulnerabilities of
machine learning approaches in adversarial settings and developing techniques …

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 …

Data compression can be achieved by reducing the dimensionality of high-dimensional but
approximately low-rank datasets, which may in fact be described by the variation of a much …

Quantum machine learning aims to execute machine learning algorithms in quantum
computers by utilizing powerful laws like superposition and entanglement for solving …

In this work we design and train deep neural networks to predict topological invariants for
one-dimensional four-band insulators in AIII class whose topological invariant is the winding …

Machine learning, the core of artificial intelligence and big data science, is one of today's
most rapidly growing interdisciplinary fields. Recently, machine learning tools and …