64 citations · 198 across the 6 of their papers we have counts for
6 papers
Guided Quantum Compression for High Dimensional Data Classification
Vasilis Belis, Patrick Odagiu, Michele Grossi +3
Quantum machine learning provides a fundamentally different approach to analyzing data. However, many interesting datasets are too complex for currently available quantum computers…
Machine Learning for Anomaly Detection in Particle Physics
Vasilis Belis, Patrick Odagiu, Thea Klæboe Årrestad
The detection of out-of-distribution data points is a common task in particle physics. It is used for monitoring complex particle detectors or for identifying rare and unexpected e…
Provable advantages of kernel-based quantum learners and quantum preprocessing based on Grover's algorithm
Till Muser, Elias Zapusek, Vasilis Belis +1
There is an ongoing effort to find quantum speedups for learning problems. Recently, [Y. Liu et al., Nat. Phys. , 1013--1017 (2021)] have proven an exponential speedup…
Unravelling physics beyond the standard model with classical and quantum anomaly detection
Julian Schuhmacher, Laura Boggia, Vasilis Belis +7
Much hope for finding new physics phenomena at microscopic scale relies on the observations obtained from High Energy Physics experiments, like the ones performed at the Large Hadr…
Quantum anomaly detection in the latent space of proton collision events at the LHC
Vasilis Belis, Kinga Anna Woźniak, Ema Puljak +7
The ongoing quest to discover new phenomena at the LHC necessitates the continuous development of algorithms and technologies. Established approaches like machine learning, along w…
Higgs analysis with quantum classifiers
Vasileios Belis, Samuel González-Castillo, Christina Reissel +4
We have developed two quantum classifier models for the classification problem, both of which fall into the category of hybrid quantum-classical algorithms fo…