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

Quantum optimal control, a toolbox for devising and implementing the shapes of external
fields that accomplish given tasks in the operation of a quantum device in the best way …

Modern quantum machine learning (QML) methods involve variationally optimizing a
parameterized quantum circuit on a training data set, and subsequently making predictions …

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 …

Quantum computers are expected to surpass the computational capabilities of classical
computers and have a transformative impact on numerous industry sectors. We present a …

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 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 …

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

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 …

Variational quantum algorithms are promising algorithms for achieving quantum advantage
on near-term devices. The quantum hardware is used to implement a variational wave …