6 papers
Lindblad engineering for quantum Gibbs state preparation under the eigenstate thermalization hypothesis
Eric Brunner, Luuk Coopmans, Gabriel Matos +3
Building upon recent progress in Lindblad engineering for quantum Gibbs state preparation algorithms, we propose a simplified protocol that is shown to be efficient under the eigen…
Measuring Correlation and Entanglement between Molecular Orbitals on a Trapped-Ion Quantum Computer
Gabriel Greene-Diniz, Chris N. Self, Michal Krompiec +4
Quantifying correlation and entanglement between molecular orbitals can elucidate the role of quantum effects in strongly correlated reaction processes. However, accurately storing…
On the Sample Complexity of Quantum Boltzmann Machine Learning
Luuk Coopmans, Marcello Benedetti
Quantum Boltzmann machines (QBMs) are machine-learning models for both classical and quantum data. We give an operational definition of QBM learning in terms of the difference in e…
Learning to generate high-dimensional distributions with low-dimensional quantum Boltzmann machines
Cenk Tüysüz, Maria Demidik, Luuk Coopmans +5
In recent years, researchers have been exploring ways to generalize Boltzmann machines (BMs) to quantum systems, leading to the development of variations such as fully-visible and…
Quixer: A Quantum Transformer Model
Nikhil Khatri, Gabriel Matos, Luuk Coopmans +1
Progress in the realisation of reliable large-scale quantum computers has motivated research into the design of quantum machine learning models. We present Quixer: a novel quantum…
Training Quantum Boltzmann Machines with the -Variational Quantum Eigensolver
Onno Huijgen, Luuk Coopmans, Peyman Najafi +2
The quantum Boltzmann machine (QBM) is a generative machine learning model for both classical data and quantum states. Training the QBM consists of minimizing the relative entropy…