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

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

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 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 …

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

We upper bound and lower bound the optimal precision with which one can estimate an
unknown Hamiltonian parameter via measurements of Gibbs thermal states with a known …

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 …

Quantum simulation of different exotic topological phases of quantum matter on a noisy
intermediate-scale quantum (NISQ) processor is attracting growing interest. Here, we …