122 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Karlsruhe Institute of TechnologyDE7 papers
- University of FreiburgDE7 papers
- RMIT UniversityAU6 papers
- Institut NéelFR5 papers
- University of StuttgartDE5 papers
- Centre National de la Recherche ScientifiqueFR4 papers
- ETH ZurichCH4 papers
- National Institutes for Quantum Science and TechnologyJP4 papers
- Universität UlmDE3 papers
- Université Grenoble AlpesFR3 papers
- Cardiff UniversityGB2 papers
- Center for Integrated Quantum Science and TechnologyDE2 papers
5 papers · 2 filters
Learning Reduced Representations for Quantum Classifiers
Patrick Odagiu, Vasilis Belis, Lennart Schulze +6
Data sets that are specified by a large number of features are currently outside the area of applicability for quantum machine learning algorithms. An immediate solution to this im…
Reconfigurable dissipative entanglement between many spin ensembles: from robust quantum sensing to many-body state engineering
Anjun Chu, Mikhail Mamaev, Martin Koppenhöfer +2
An attractive approach for stabilizing entangled many-body spin states is to employ engineered dissipation. Most existing proposals either target relatively simple collective spin…
Readout of a solid state spin ensemble at the projection noise limit
Rouven Maier, Cheng-I Ho, Andrej Denisenko +4
Spin ensembles are central to quantum science, from frequency standards and fundamental physics searches to magnetic resonance spectroscopy and quantum sensing. Their performance i…
Extrapolation method to optimize linear-ramp QAOA parameters: Evaluation of QAOA runtime scaling
Vanessa Dehn, Martin Zaefferer, Gerhard Hellstern +3
The Quantum Approximate Optimization Algorithm (QAOA) has been suggested as a promising candidate for the solution of combinatorial optimization problems. Yet, whether - or under w…
Learning agent-based approach to the characterization of open quantum systems
Lorenzo Fioroni, Ivan Rojkov, Florentin Reiter
Characterizing quantum processes is crucial for the execution of quantum algorithms on available quantum devices. A powerful framework for this purpose is the Quantum Model Learnin…