Quantum computers offer an intriguing path for a paradigmatic change of computing in the
natural sciences and beyond, with the potential for achieving a so-called quantum …

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 …

AFV CoverSheet Page 1 LA-UR-23-30483 Accepted Manuscript A Lie algebraic theory of
barren plateaus for deep parameterized quantum circuits Cerezo de la Roca, Marco Vinicio …

Quantum neural network architectures that have little to no inductive biases are known to
face trainability and generalization issues. Inspired by a similar problem, recent …

Despite the great promise of quantum machine learning models, there are several
challenges one must overcome before unlocking their full potential. For instance, models …

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 …

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

Quantum generative models provide inherently efficient sampling strategies and thus show
promise for achieving an advantage using quantum hardware. In this work, we investigate …

Nonstabilizerness or magic resource characterizes the amount of non-Clifford operations
needed to prepare quantum states. It is a crucial resource for quantum computing and a …