40 citations · 41 across the 8 of their papers we have counts for
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Subspace Controllability in Variational Quantum Circuits: Maximising Expressivity and Increasing Search Efficiency with Dynamical Lie Algebras
Andrew Rowan Barlow, Hans-Martin Rieser, Markus Lange
Variational hybrid quantum models are a common paradigm for realising machine learning on quantum hardware. Nonetheless, they are challenging to train due to several problems intri…
Quantum Tensor Network Learning with DMRG
Gustav J L Jäger, Martin B Plenio, Hans-Martin Rieser
Tensor Networks are a relatively new machine learning approach. The architectures proposed initially are inspired by approaches from quantum many-body physics simulations. One comm…
Entanglement is Half the Story: Post-Selection vs. Partial Traces
Gustav J L Jäger, Krzysztof Bieniasz, Martin B Plenio +1
While tensor networks have their traditional application in simulating quantum systems, in the recent decade they have gathered interest as machine learning models. We combine the…
Kernel-based optimization of measurement operators for quantum reservoir computers
Markus Gross, Hans-Martin Rieser
Finding optimal measurement operators is crucial for the performance of quantum reservoir computers (QRCs), since they employ a fixed quantum feature map. We formulate the training…
Theory and interpretability of Quantum Extreme Learning Machines: a Pauli transfer matrix approach
Markus Gross, Hans-Martin Rieser
Quantum reservoir computers (QRCs) have emerged as a promising approach to quantum machine learning, since they utilize the natural dynamics of quantum systems for data processing…
A Hybrid Quantum Solver for Gaussian Process Regression
Kerem Bükrü, Steffen Leger, M. Lautaro Hickmann +3
Gaussian processes are widely known for their ability to provide probabilistic predictions in supervised machine learning models. Their non-parametric nature and flexibility make t…
Hybrid quantum tensor networks for aeroelastic applications
M. Lautaro Hickmann, Pedro Alves, David Quero +2
We investigate the application of hybrid quantum tensor networks to aeroelastic problems, harnessing the power of Quantum Machine Learning (QML). By combining tensor networks with…
Tensor networks for quantum machine learning
Hans-Martin Rieser, Frank Köster, Arne Peter Raulf
Once developed for quantum theory, tensor networks have been established as a successful machine learning paradigm. Now, they have been ported back to the quantum realm in the emer…