Quantum machine learning is a rapidly growing field at the intersection of quantum
technology and artificial intelligence. This review provides a two-fold overview of several key …

The quantification of the entanglement present in a physical system is of paramount
importance for fundamental research and many cutting-edge applications. Now, achieving …

A nonlocal quantum approach is presented to polarimetry, leveraging the phenomenon of
entanglement in photon pairs to enhance the precision in sample property determination. By …

We determine the resource scaling of machine learning-based quantum state reconstruction
methods, in terms of inference and training, for systems of up to four qubits when …

We consider the properties of a specific distribution of mixed quantum states of arbitrary
dimension that can be biased towards a specific mean purity. In particular, we analyze …

Thin-Film Lithium Niobate is an emerging integrated photonic platform showing great
promise due to its large second-order nonlinearity at microwave and optical frequencies …

We propose a series of data-centric heuristics for improving the performance of machine
learning systems when applied to problems in quantum information science. In particular …

In this article, we introduce a framework for Hamiltonian tomography of multiqubit systems
with random noise. We adopt the quantum quench protocol to reconstruct a many-body …

In recent years, neural network estimator-based quantum state tomography has gained its
popularity. Inspired by advances in the field of state-of-the-art deep learning techniques, we …

Quantum networks exploit the unique properties of quantum mechanics to enable
communication and networking tasks unavailable to existing distributed classical systems …