News & Publications
Recent highlights and publications
Three recent unpublished works (selection):
Exponential separations between classical and quantum learners
One liner: How can we prove quantum learners can exponentially outperform classical learners in hard learning problems stemming e.g. from quantum experiments in condensed matter, or high-energy physics? Answer here: link.
Shadows of quantum machine learning
One liner: Can there be a provable advantage for quantum machine learning, where only the training, but not the deployment requires a quantum computer? Answer here: link.
Quantifying quantum advantage in topological data analysis
One liner: Can quantum topological data analysis lead to real-world (superpolynomial) quantum advantages? Answer here: link.
Recent published highlights:
Jan '23: paper "Quantum machine learning beyond kernel methods" (published version, arXiv) proves inclusions and separations between all main QML models, published in Nat. Commun.
Nov '22: paper "Towards quantum advantage via topological data analysis" (published version, arXiv)
providing the first proofs of speed-ups for a generalization of the topological data analysis problem, published in Quantum.
Dec '21: paper "Parametrized quantum policies for reinforcement learning" (published version, arXiv), the first to prove classical/quantum learning separations in reinforcement learning with classical environments is published in NeurIPS '21.
Dec '21: paper "Reinforcement learning for optimization of variational quantum circuit architectures" (published version, arXiv), where reinforcement learning designs suitable circuits, is published in NeurIPS '21.
Mar '21: paper "Experimental speed-up up in reinforcement learning agents" (published version, arXiv) done in an international collaboration with a number of universities, institutes and companies is published in Nature.
Recent popular media coverage:
New Scientist Feb '23 covered our work on running larger QML circuits on smaller quantum computers.
Quanta Magazine Dec '22 covered our recent work on quantum topological data analysis.
Quanta Magazine Feb '22 covered our work on quantum reinforcement learning.
The full list of publications can be found here.
All my publications are available on arXiv, and scientometrics can be found on my google scholar pages.
You can download my (relatively recent) CV here.
Upcoming conferences (under construction)
I will be giving talks at the following upcoming conferences and events:
QIP (2023)