At the intersection of machine learning and quantum computing, quantum machine learning
has the potential of accelerating data analysis, especially for quantum data, with …

The variational quantum eigensolver (or VQE), first developed by Peruzzo et al.(2014), has
received significant attention from the research community in recent years. It uses the …

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 …

Parametrized quantum circuits serve as ansatze for solving variational problems and
provide a flexible paradigm for the programming of near-term quantum computers. Ideally …

Quantum machine learning (QML) models are aimed at learning from data encoded in
quantum states. Recently, it has been shown that models with little to no inductive biases (ie …

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 …

The prospect of achieving quantum advantage with quantum neural networks (QNNs) is
exciting. Understanding how QNN properties (for example, the number of parameters M) …

Variational quantum algorithms, a popular heuristic for near-term quantum computers, utilize
parameterized quantum circuits which naturally express Lie groups. It has been postulated …

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 …