For quantum computers to successfully solve real-world problems, it is necessary to tackle
the challenge of noise: the errors that occur in elementary physical components due to …

Recent years have seen the rapid growth and development of the field of smart photonics,
where machine-learning algorithms are being matched to optical systems to add new …

Let there be light–to change the world we want to be! Over the past several decades, and
ever since the birth of the first laser, mankind has witnessed the development of the science …

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

Quantum metrology is one of the most promising applications of quantum technologies. The
aim of this research field is the estimation of unknown parameters exploiting quantum …

Deep learning in the context of nano-photonics is mostly discussed in terms of its potential
for inverse design of photonic devices or nano-structures. Many of the recent works on …

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 …

Optical computing promises to improve the speed and energy efficiency of machine learning
applications,,,,–. However, current approaches to efficiently train these models are limited by …

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 …

Quantum state tomography (QST) is a challenging task in intermediate-scale quantum
devices. Here, we apply conditional generative adversarial networks (CGANs) to QST. In the …