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 …

Abstract Machine learning has achieved dramatic success in a broad spectrum of
applications. Its interplay with quantum physics may lead to unprecedented perspectives for …

Variational quantum computing schemes train a loss function by sending an initial state
through a parametrized quantum circuit, and measuring the expectation value of some …

Abstract Variational Quantum Algorithms (VQAs) have received considerable attention due
to their potential for achieving near-term quantum advantage. However, more work is …

A large amount of effort has recently been put into understanding the barren plateau
phenomenon. In this perspective article, we face the increasingly loud elephant in the room …

Abstract Kernel methods in Quantum Machine Learning (QML) have recently gained
significant attention as a potential candidate for achieving a quantum advantage in data …

Kernel methods in Quantum Machine Learning (QML) have recently gained significant
attention as a potential candidate for achieving a quantum advantage in data analysis …

Several architectures have been proposed for quantum neural networks (QNNs), with the
goal of efficiently performing machine learning tasks on quantum data. Rigorous scaling …

We characterize the variational power of quantum circuit tensor networks in the
representation of physical many-body ground states. Such tensor networks are formed by …

Parametrized quantum circuits can be used as quantum neural networks and have the
potential to outperform their classical counterparts when trained for addressing learning …