Speakers
and Program

David
Wierichs
Xanadu Quantum Technologies – Toronto, Canada

Gradient-based training of QML models with PennyLane

Quantum machine learning (QML) is an exciting field in modern research. Parameterized quantum circuits are frequently used to construct QML models, which then are trained, predominantly using gradient-based optimization techniques.
In the hands-on part of the session we will explore PennyLane, a versatile cross-platform Python library for differentiable quantum programming, for constructing and training QML models. We will discuss architectures tailored to specific applications, harvesting most recent research on QML.
In a more theoretical part of the session, we will learn about differentiation of parameterized quantum circuits, which is a quintessential subroutine of established QML workflows, both on simulators and quantum hardware.

Matthias C.
Caro
Freie Universität Berlin – Berlin, Germany

Hamiltonian Learning and Testing

Hamiltonians play a central role in quantum physics because they describe how closed quantum systems evolve over time. Therefore, characterizing an unknown Hamiltonian – or at least determining some of its properties – when given access to the corresponding time evolution, or to copies of the Gibbs state describing a thermalized system, is a task of fundamental interest. Additionally, with early quantum devices emerging, it becomes a technologically relevant task, with implications for benchmarking in quantum simulation and quantum computing. In this quantum learning theory lecture, I will provide an introduction to the vibrant research area of Hamiltonian learning and testing, with a focus on recent work proving rigorous learning and testing guarantees.

Topics to be announced

Marco
Cerezo
Los Alamos National Laboratory – Los Alamos, NM, USA
Simone
Montangero
University of Padua – Padua, Italy
Christa
Zoufal
IBM Quantum – Switzerland
David
Wierichs
Xanadu Quantum Technologies – Toronto, Canada
Daniel K.
Park
Yonsei University – Seoul, South Korea
Vladislav
Golyanik
Max-Planck-Institut für Informatik – Saarbrücken, Germany
Michele
Grossi
CERN – Geneva, Switzerland
More speakers
coming soon!