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

We present an extensive introduction to quantum collision models (CMs), also known as
repeated interactions schemes: a class of microscopic system–bath models for investigating …

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 …

Machine learning (ML) has emerged as a formidable force for identifying hidden but
pertinent patterns within a given data set with the objective of subsequent generation of …

The field of classical stochastic processes forms a major branch of mathematics. Stochastic
processes are, of course, also very well studied in biology, chemistry, ecology, geology …

The notion of non-Hermitian optics and photonics rooted in quantum mechanics and
photonic systems has recently attracted considerable attention ushering in tremendous …

Abstract The GRadient Ascent Pulse Engineering (GRAPE) method is widely used for
optimization in quantum control. GRAPE is gradient search method based on exact …

Quantum machine learning has emerged as an exciting and promising paradigm inside
quantum technologies. It may permit, on the one hand, to carry out more efficient machine …

Non-Markovian noise presents a particularly relevant challenge in understanding and
combating decoherence in quantum computers, yet is challenging to capture in terms of …

Exact numerical simulations of dynamics of open quantum systems often require immense
computational resources. We demonstrate that a deep artificial neural network composed of …