Recent advances in classical machine learning have shown that creating models with
inductive biases encoding the symmetries of a problem can greatly improve performance …

We present a learning algorithm for discovering conservation laws given as sums of
geometrically local observables in quantum dynamics. This includes conserved quantities …

We discuss here some aspects related to the symmetries of a quantum many-body problem
when trying to treat it on a quantum computer. Several features related to symmetry …

Symmetry is a unifying concept in physics. In quantum information and beyond, it is known
that quantum states possessing symmetry are not useful for certain information-processing …

Symmetry is an important and unifying notion in many areas of physics. In quantum
mechanics, it is possible to eliminate degrees of freedom from a system by leveraging …

In quantum computing, knowing the symmetries a given system or state obeys or disobeys is
often useful. For example, Hamiltonian symmetries may limit allowed state transitions or …

We consider the problems of testing and learning an unknown $ n $-qubit Hamiltonian $ H $
from queries to its evolution operator $ e^{-iHt} $ under the normalized Frobenius norm. We …

Many properties of Boolean functions can be tested far more efficiently than the function can
be learned. However, this advantage often disappears when testers are limited to random …

Locality is a fundamental feature of many physical time evolutions. Assumptions on locality
and related structural properties also underlie recently proposed procedures for learning an …

Symmetry plays a crucial role in quantum physics, dictating the behavior and dynamics of
physical systems. In this paper, We develop a hypothesis-testing framework for quantum …