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

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 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 …

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 …

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 …

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

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