| What aspects of
machine learning and AI benefit the most from quantum
computers? From low polynomial
improvements for various aspects of machine learning, reinforcement
learning and AI, to more exotic learning algorithms
with a potentially BQP-complete flavor, this research line investigates
the perspectives and limitations of quantum enhancements for machine
learning and AI.
|
| Will
advanced ML and AI techniques be truly integral in building
scalable quantum computers? Modern machine
learning and AI methods may be critical for hard control problems,
problems which require on-line and run-time decisions, and may become a
vital tool to help us come up with new creative and counter-intuitive
solutions to problems which arise in quantum computing.
|
| How can quantum
computing advance our methods for solving ubiquitous yet
hard computational problems? Not everything
useful has useful lower bounds, and very often, especially
in ever more important AI domains, heuristics and in
particular quantum heuristic methods will be the only way to go.
|
In the Quantum@LIACS team we combine the purely theoretical aspects of the questions above, with research in implementations on near-term quantum devices.
More details on my background can be found here.
You can download my (relatively recent) CV
here.
More about my publications can be found here.