Driven by the rapid progress in quantum hardware, recent years have witnessed a furious
race for quantum technologies in both academia and industry. Universal quantum …

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 …

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 …

Despite its undeniable success, classical machine learning remains a resource-intensive
process. Practical computational efforts for training state-of-the-art models can now only be …

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 …

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 …

Supervised machine learning is the classification of new data based on already classified
training examples. In this work, we show that the support vector machine, an optimized …

Machine-learning tasks frequently involve problems of manipulating and classifying large
numbers of vectors in high-dimensional spaces. Classical algorithms for solving such …

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 …