In this short review article, we aim to provide physicists not working within the quantum
computing community a hopefully easy-to-read introduction to the state of the art in the field …

Quantum information technologies, on the one hand, and intelligent learning systems, on the
other, are both emergent technologies that are likely to have a transformative impact on our …

Machine learning is frequently listed among the most promising applications for quantum
computing. This is in fact a curious choice: the machine-learning algorithms of today are …

Neural networks enjoy widespread success in both research and industry and, with the
advent of quantum technology, it is a crucial challenge to design quantum neural networks …

Fuelled by increasing computer power and algorithmic advances, machine learning
techniques have become powerful tools for finding patterns in data. Quantum systems …

Machine learning algorithms learn a desired input-output relation from examples in order to
interpret new inputs. This is important for tasks such as image and speech recognition or …

Quantum Machine Learning bridges the gap between abstract developments in quantum
computing and the applied research on machine learning. Paring down the complexity of the …

Machine Learning (ML) aims at designing models that learn from previous experience,
without being explicitly formulated. Applications of machine learning are inexhaustible …

The quest for quantum advantage, wherein quantum computers surpass the computational
capabilities of classical computers executing state-of-the-art algorithms on well-defined …

Anomaly detection is used for identifying data that deviate from “normal” data patterns. Its
usage on classical data finds diverse applications in many important areas such as finance …