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 …

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 …

In these Lecture Notes, we provide a comprehensive introduction to the most recent
advances in the application of machine learning methods in quantum sciences. We cover …

Establishing the nature of the ground state of the Heisenberg antiferromagnet (HAFM) on the
kagome lattice is well-known to be a prohibitively difficult problem for classical computers …

Inference is the task of drawing conclusions about unobserved variables given observations
of related variables. Applications range from identifying diseases from symptoms to …

Entropy measures quantify the amount of information and correlation present in a quantum
system. In practice, when the quantum state is unknown and only copies thereof are …

We present a variational density matrix approach to the thermal properties of interacting
fermions in the continuum. The variational density matrix is parametrized by a permutation …

Efficiently learning an unknown Hamiltonian given access to its dynamics is a problem of
interest for quantum metrology, many-body physics and machine learning. A fundamental …

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 …

The properties of strongly-coupled lattice gauge theories at finite density as well as in real
time have largely eluded first-principles studies on the lattice. This is due to the failure of …