188 citations · 191 across the 6 of their papers we have counts for
6 papers
Cautious optimism for deep parameterized quantum circuits
Marie Kempkes, Elies Gil-Fuster, Carlos Bravo-Prieto +5
A central challenge in quantum machine learning is understanding the scaling behavior of parameterized quantum circuits (PQCs). In particular, it remains unclear how their performa…
Reinforcement learning of quantum circuit architectures for molecular potential energy curves
Maureen Krumtünger, Alissa Wilms, Paul K. Faehrmann +4
Quantum chemistry and optimization are two of the most prominent applications of quantum computers. Variational quantum algorithms have been proposed for solving problems in these…
Quantum reinforcement learning of classical rare dynamics: Enhancement by intrinsic Fourier features
Alissa Wilms, Laura Ohff, Andrea Skolik +3
Rare events are essential for understanding the behavior of non-equilibrium and industrial systems. It is of ongoing interest to develop methods for effectively searching for rare…
Distribution and volume based scoring for Isolation Forests
Hichem Dhouib, Alissa Wilms, Paul Boes
We make two contributions to the Isolation Forest method for anomaly and outlier detection. The first contribution is an information-theoretically motivated generalisation of the s…
Big Bang and Topology
Torsten Asselmeyer-Maluga, Jerzy Krol, Alissa Wilms
In this paper we discuss the initial state of the universe at the Big Bang. By using ideas of Freedman in the proof of the disk embedding theorem for 4-manifolds, we describe the c…
Exploiting symmetry in variational quantum machine learning
Johannes Jakob Meyer, Marian Mularski, Elies Gil-Fuster +4
Variational quantum machine learning is an extensively studied application of near-term quantum computers. The success of variational quantum learning models crucially depends on f…