The development of quantum computing across several technologies and platforms has
reached the point of having an advantage over classical computers for an artificial problem …

Programmable quantum simulators and quantum computers are opening unprecedented
opportunities for exploring and exploiting the properties of highly entangled complex …

One of the most important properties of classical neural networks is how surprisingly
trainable they are, though their training algorithms typically rely on optimizing complicated …

The future development of quantum technologies relies on creating and manipulating
quantum systems of increasing complexity, with key applications in computation, simulation …

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 …

We study the problem of learning the parameters for the Hamiltonian of a quantum many-
body system, given limited access to the system. In this work, we build upon recent …

Strongly coupled gauge theories far from equilibrium may exhibit unique features that could
illuminate the physics of the early universe and of hadron and ion colliders. Studying real …

Entanglement is the crucial ingredient of quantum many-body physics, and characterizing
and quantifying entanglement in the closed-system dynamics of quantum simulators …