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 …

The Mpemba effect is a counterintuitive phenomena in which a hot system reaches a cold
temperature faster than a colder system, under otherwise identical conditions. Here, we …

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 …

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

As progress is made towards the first generation of error-corrected quantum computers,
robust characterization and validation protocols are required to assess the noise …

Large-scale quantum devices provide insights beyond the reach of classical simulations.
However, for a reliable and verifiable quantum simulation, the building blocks of the …

Characterizing noisy quantum devices requires methods for learning the underlying
quantum Hamiltonian which governs their dynamics. Often, such methods compare …

The state of a quantum system may be steered towards a predesignated target state,
employing a sequence of weak blind measurements (where the detector's readouts are …

It is known that protocols based on weak measurements can be used to steer quantum
systems into predesignated pure states. Here we show that weak-measurement-based …

Hamiltonian Learning (HL) is essential for validating quantum systems in quantum
computing. Not all Hamiltonians can be uniquely recovered from a steady state. HL success …