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
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