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 …

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 …

Neural network-based machine learning has recently proven successful for many complex
applications ranging from image recognition to precision medicine. However, its direct …

Building a large-scale quantum computer requires effective strategies to correct errors that
inevitably arise in physical quantum systems. Quantum error-correction codes present a way …

Pauli channels are ubiquitous in quantum information, both as a dominant noise source in
many computing architectures and as a practical model for analyzing error correction and …

In the scale-up of quantum computers, the framework underpinning fault-tolerance generally
relies on the strong assumption that environmental noise affecting qubit logic is uncorrelated …

Quantum error correction is widely thought to be the key to fault-tolerant quantum
computation. However, determining the most suited encoding for unknown error channels or …

Over the past few years, machine learning has emerged as a powerful computational tool to
tackle complex problems in a broad range of scientific disciplines. In particular, artificial …

The color code is a topological quantum error-correcting code supporting a variety of
valuable fault-tolerant logical gates. Its two-dimensional version, the triangular color code …

Matching algorithms can be used for identifying errors in quantum systems, being the most
famous the Blossom algorithm. Recent works have shown that small distance quantum error …