Quantum computers are next-generation devices that hold promise to perform calculations
beyond the reach of classical computers. A leading method towards achieving this goal is …

We argue that an excess in entanglement between the visible and hidden units in a
quantum neural network can hinder learning. In particular, we show that quantum neural …

Learning the Hamiltonian that describes interactions in a quantum system is an important
task in both condensed-matter physics and the verification of quantum technologies. Its …

This work presents a novel realization approach to quantum Boltzmann machines (QBMs).
The preparation of the required Gibbs states, as well as the evaluation of the loss function's …

Abstract Quantum Boltzmann machines (QBMs) are machine-learning models for both
classical and quantum data. We give an operational definition of QBM learning in terms of …

Quantum algorithms have been successfully applied to provide computational speed ups to
various machine-learning tasks and methods. A notable exception to this has been deep …

Quantum machine learning has the potential for broad industrial applications, and the
development of quantum algorithms for improving the performance of neural networks is of …

Quantum neural networks (QNNs) are a framework for creating quantum algorithms that
promises to combine the speedups of quantum computation with the widespread successes …

We propose the first near-optimal quantum algorithm for estimating in Euclidean norm the
mean of a vector-valued random variable with finite mean and covariance. Our result aims at …

Within the last several years quantum machine learning (QML) has begun to mature;
however, many open questions remain. Rather than review open questions, in this …