151 citations · 208 across the 5 of their papers we have counts for
5 papers
Single-component gradient rules for variational quantum algorithms
Thomas Hubregtsen, Frederik Wilde, Shozab Qasim +1
Many near-term quantum computing algorithms are conceived as variational quantum algorithms, in which parameterized quantum circuits are optimized in a hybrid quantum-classical set…
Training Quantum Embedding Kernels on Near-Term Quantum Computers
Thomas Hubregtsen, David Wierichs, Elies Gil-Fuster +3
Kernel methods are a cornerstone of classical machine learning. The idea of using quantum computers to compute kernels has recently attracted attention. Quantum embedding kernels (…
Evaluation of Parameterized Quantum Circuits: on the relation between classification accuracy, expressibility and entangling capability
Thomas Hubregtsen, Josef Pichlmeier, Patrick Stecher +1
An active area of investigation in the search for quantum advantage is Quantum Machine Learning. Quantum Machine Learning, and Parameterized Quantum Circuits in a hybrid quantum-cl…
Integration and Evaluation of Quantum Accelerators for Data-Driven User Functions
Thomas Hubregtsen, Christoph Segler, Josef Pichlmeier +3
Quantum computers hold great promise for accelerating computationally challenging algorithms on noisy intermediate-scale quantum (NISQ) devices in the upcoming years. Much attentio…
Quantum Computer Architecture: Towards Full-Stack Quantum Accelerators
K. Bertels, A. Sarkar, A. A. Mouedenne +4
This paper presents the definition and implementation of a quantum computer architecture to enable creating a new computational device - a quantum computer as an accelerator. In th…