27 citations · 63 across the 7 of their papers we have counts for
6 papers · 1 filter
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…
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-…
Classification of the Fashion-MNIST Dataset on a Quantum Computer
Kevin Shen, Bernhard Jobst, Elvira Shishenina +1
The potential impact of quantum machine learning algorithms on industrial applications remains an exciting open question. Conventional methods for encoding classical data into quan…
Efficient MPS representations and quantum circuits from the Fourier modes of classical image data
Bernhard Jobst, Kevin Shen, Carlos A. Riofrío +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…