Krems group website

Quantum machine learning

We explore the interplay of quantum computing and machine learning. Can quantum computing enhance machine learning? How to build the most optimal quantum models? Does quantum machine learning have quantum advantage?

Universality of quantum kernels
Bayesian design of quantum circuits
Quantum Gaussian processes
Quantum vs classical kernels for regression

Machine learning for quantum problems

We develop algorithms to explore physics by machine learning and to build physics into machine learning for applications from quantum condensed matter to molecular dynamics.

Extrapolation across quantum phase transitions
Bayesian optimization for inverse quantum problems
Quantum transport through qubit networks

Quantum scattering theory

Our calculations explore new regimes of quantum scattering: universality of diffractive scattering, molecular collisions in fields, probabilistic predictions of scattering observables.

Self-calibrating quantum pressure standard
Universality of probabilistic predictions
Zeeman predissociation