Applications such as simulating complicated quantum systems or solving large-scale linear
algebra problems are very challenging for classical computers, owing to the extremely high …

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

Abstract Variational Quantum Algorithms (VQAs) may be a path to quantum advantage on
Noisy Intermediate-Scale Quantum (NISQ) computers. A natural question is whether noise …

Quantum computers can be used for supervised learning by treating parametrized quantum
circuits as models that map data inputs to predictions. While a lot of work has been done to …

Quantum neural networks (QNNs) have generated excitement around the possibility of
efficiently analyzing quantum data. But this excitement has been tempered by the existence …

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 …

Our world, of course, changed in many other ways as well since the publication of the first
edition. The global pandemic impacted all areas of society and will probably transform how …

Quantum learning theory is a new and very active area of research at the intersection of
quantum computing and machine learning. Important breakthroughs in the past two years …

Learning a many-body Hamiltonian from its dynamics is a fundamental problem in physics.
In this Letter, we propose the first algorithm to achieve the Heisenberg limit for learning an …

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 …