Publications (9)
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…
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…
Crossing a topological phase transition with a quantum computer
Adam Smith, Bernhard Jobst, Andrew G. Green +1
Quantum computers promise to perform computations beyond the reach of modern computers with profound implications for scientific research. Due to remarkable technological advances,…
Skeleton of Matrix-Product-State-Solvable Models Connecting Topological Phases of Matter
Nick G. Jones, Julian Bibo, Bernhard Jobst +3
Models whose ground states can be written as an exact matrix product state (MPS) provide valuable insights into phases of matter. While MPS-solvable models are typically studied as…
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…
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…