We study the performance of classical and quantum machine learning (ML) models in
predicting outcomes of physical experiments. The experiments depend on an input …

The use of quantum computing for machine learning is among the most exciting prospective
applications of quantum technologies. However, machine learning tasks where data is …

The ultimate limits for the quantum machine learning of quantum data are investigated by
obtaining a generalisation of the celebrated No Free Lunch (NFL) theorem. We find a lower …

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

We present an efficient machine-learning (ML) algorithm for predicting any unknown
quantum process E over n qubits. For a wide range of distributions D on arbitrary n-qubit …

Abstract Machine learning algorithms based on parametrized quantum circuits are prime
candidates for near-term applications on noisy quantum computers. In this direction, various …

Quantum machine learning is often highlighted as one of the most promising practical
applications for which quantum computers could provide a computational advantage …

It has been hypothesized that quantum computers may lend themselves well to applications
in machine learning. In the present work, we analyze function classes defined via quantum …

Generalisation in machine learning often relies on the ability to encode structures present in
data into an inductive bias of the model class. To understand the power of quantum machine …

The advent of noisy intermediate-scale quantum computers has put the search for possible
applications to the forefront of quantum information science. One area where hopes for an …