The future development of quantum technologies relies on creating and manipulating
quantum systems of increasing complexity, with key applications in computation, simulation …

Machine learning (ML) encompasses a broad range of algorithms and modeling tools used
for a vast array of data processing tasks, which has entered most scientific disciplines in …

Controlling and programming quantum devices to process quantum information by the unit
of quantum dit, ie, qudit, provides the possibilities for noise-resilient quantum …

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 ability to control multidimensional quantum systems is central to the development of
advanced quantum technologies. We demonstrate a multidimensional integrated quantum …

Quantum networks provide the framework for quantum communication, clock
synchronization, distributed quantum computing, and sensing. Implementing large-scale …

During the last ten years, superconducting circuits have passed from being interesting
physical devices to becoming contenders for near-future useful and scalable quantum …

Noise is the central obstacle to building large-scale quantum computers. Quantum systems
with sufficiently uncorrelated and weak noise could be used to solve computational …

Entanglement is the crucial ingredient of quantum many-body physics, and characterizing
and quantifying entanglement in the closed-system dynamics of quantum simulators …

There has been recent promising experimental and theoretical evidence that quantum
computational tools might enhance the precision and efficiency of physical experiments …