11 citations · 14 across the 6 of their papers we have counts for
5 papers · 1 filter
Quantifying the Value of Privileged Information Using a PAC-Bayesian Approach
Vasily Bokov, Sebastian Schmitt, Vedran Dunjko +1
In practice, various learning scenarios provide access to auxiliary features exclusively during training. Incorporating such data to enhance model performance gave rise to a paradi…
Comparing Qubit and Qudit Encodings for EV Charging and Trip Assignment Problems
Linus Ekstrøm, Hao Wang, Sebastian Schmitt
Variational quantum algorithms have attracted attention for their potential to solve combinatorial optimization problems. We study how the choice of encoding affects the resource r…
Improving Quantum Multi-Objective Optimization with Archiving and Substitution
Linus Ekstrøm, Takafumi Hosogi, Xavier Bonet-Monroig +3
Finding optimal solutions of conflicting objectives is a daily matter in many industrial applications, with multi-objective optimization trying to find the best solutions to them.…
Enabling Low-Latency Machine learning on Radiation-Hard FPGAs with hls4ml
Katya Govorkova, Julian Garcia Pardinas, Vladimir Loncar +5
This paper presents an end-to-end demonstration of a viable, ultra-fast, radiation-hard machine learning (ML) application on FPGAs, which could be used in future high-energy physic…
Machine learning with minimal use of quantum computers: Provable advantages in Learning Under Quantum Privileged Information (LUQPI)
Vasily Bokov, Lisa Kohl, Sebastian Schmitt +1
Quantum machine learning (QML) is often listed as a promising candidate for useful applications of quantum computers, in part due to numerous proofs of possible quantum advantages.…