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 …

We study the problem of learning a local quantum Hamiltonian H given copies of its Gibbs
state ρ= e− β H/(e− β H) at a known inverse temperature β> 0. Anshu, Arunachalam …

We study the problem of learning a Hamiltonian H to precision ε, supposing we are given
copies of its Gibbs state ρ=\exp(-βH)/Tr(\exp(-βH)) at a known inverse temperature β. Anshu …

We study the problem of learning the Hamiltonian of a many-body quantum system from
experimental data. We show that the rate of learning depends on the amount of control …

We develop a protocol for learning a class of interacting bosonic Hamiltonians from
dynamics with Heisenberg-limited scaling. For Hamiltonians with an underlying bounded …

Hamiltonian learning, as an important quantum machine learning technique, provides a
significant approach for determining an accurate quantum system. This paper establishes …

Characterizing the interactions and dynamics of quantum mechanical systems is an
essential task in developing quantum technologies. We propose an efficient protocol based …

Despite fundamental interests in learning quantum circuits, the existence of a
computationally efficient algorithm for learning shallow quantum circuits remains an open …

The goal of digital quantum simulation is to approximate the dynamics of a given target
Hamiltonian via a sequence of quantum gates, a procedure known as Trotterization. The …

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