12 citations · 12 across the 3 of their papers we have counts for
3 papers
Exponential Scaling Barriers for Variational Quantum Eigensolvers
Manuel Hagelueken, David A. Kreplin, Florian Wieland +2
The Variational Quantum Eigensolver (VQE) is widely regarded as a promising algorithm for calculating ground states of quantum systems that are intractable for classical computers.…
Data Efficient Prediction of excited-state properties using Quantum Neural Networks
Manuel Hagelüken, Marco F. Huber, Marco Roth
Understanding the properties of excited states of complex molecules is crucial for many chemical and physical processes. Calculating these properties is often significantly more re…
Reinforcement learning-based architecture search for quantum machine learning
Frederic Rapp, David A. Kreplin, Marco F. Huber +1
Quantum machine learning models use encoding circuits to map data into a quantum Hilbert space. While it is well known that the architecture of these circuits significantly influen…