6 papers
Computable fermionic non-Gaussianity from the covariance matrix
Poetri Sonya Tarabunga, Bernhard Jobst, Raúl Morral-Yepes +4
Fermionic non-Gaussianity, or fermionic magic, is a key resource underlying the computational complexity of fermionic quantum systems, yet tractable and operationally meaningful wa…
Typical Machine Learning Datasets as Low-Depth Quantum Circuits
Florian J. Kiwit, Bernhard Jobst, Andre Luckow +2
Quantum machine learning (QML) is an emerging field that investigates the capabilities of quantum computers for learning tasks. While QML models can theoretically offer advantages…
Probing non-equilibrium topological order on a quantum processor
M. Will, T. A. Cochran, E. Rosenberg +6
Out-of-equilibrium phases in many-body systems constitute a new paradigm in quantum matter - they exhibit dynamical properties that may otherwise be forbidden by equilibrium thermo…
Quantum Entrepreneurship Lab: Training a Future Workforce for the Quantum Industry
Aaron Sander, Rosaria Cercola, Andrea Capogrosso +9
The Quantum Entrepreneurship Lab (QEL) is a one-semester, project-based course at the Technical University of Munich (TUM), designed to bridge the gap between academic research and…
Visualizing Dynamics of Charges and Strings in (2+1)D Lattice Gauge Theories
Tyler A. Cochran, Bernhard Jobst, Eliott Rosenberg +189
Lattice gauge theories (LGTs) can be employed to understand a wide range of phenomena, from elementary particle scattering in high-energy physics to effective descriptions of many-…
Efficient MPS representations and quantum circuits from the Fourier modes of classical image data
Bernhard Jobst, Kevin Shen, Carlos A. RiofrÃo +2
Machine learning tasks are an exciting application for quantum computers, as it has been proven that they can learn certain problems more efficiently than classical ones. Applying…