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

Quantum information technologies, on the one hand, and intelligent learning systems, on the
other, are both emergent technologies that are likely to have a transformative impact on our …

Fuelled by increasing computer power and algorithmic advances, machine learning
techniques have become powerful tools for finding patterns in data. Quantum systems …

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 …

Generalization bounds are a critical tool to assess the training data requirements of
Quantum Machine Learning (QML). Recent work has established guarantees for in …

Machine learning algorithms learn a desired input-output relation from examples in order to
interpret new inputs. This is important for tasks such as image and speech recognition or …

Learning the Hamiltonian that describes interactions in a quantum system is an important
task in both condensed-matter physics and the verification of quantum technologies. Its …

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 efficient characterization of quantum systems,,, the verification of the operations of
quantum devices,, and the validation of underpinning physical models,,, are central …

Recovering an unknown Hamiltonian from measurements is an increasingly important task
for certification of noisy quantum devices and simulators. Recent works have succeeded in …