Pennylane: Automatic differentiation of hybrid quantum-classical computations arXiv preprint arXiv:1811.04968, 2018 | 934 | 2018 |
Unsupervised phase discovery with deep anomaly detection K Kottmann, P Huembeli, M Lewenstein, A Acín Physical Review Letters 125 (17), 170603, 2020 | 82 | 2020 |
Unsupervised machine learning of topological phase transitions from experimental data N Käming, A Dawid, K Kottmann, M Lewenstein, K Sengstock, A Dauphin, ... Machine Learning: Science and Technology 2 (3), 035037, 2021 | 77 | 2021 |
Variational quantum anomaly detection: Unsupervised mapping of phase diagrams on a physical quantum computer K Kottmann, F Metz, J Fraxanet, N Baldelli Physical Review Research 3 (4), 043184, 2021 | 38 | 2021 |
Supersolid-superfluid phase separation in the extended Bose-Hubbard model K Kottmann, A Haller, A Acín, GE Astrakharchik, M Lewenstein Physical Review B 104 (17), 174514, 2021 | 18 | 2021 |
Detecting ergodic bubbles at the crossover to many-body localization using neural networks T Szołdra, P Sierant, K Kottmann, M Lewenstein, J Zakrzewski Physical Review B 104 (14), L140202, 2021 | 15 | 2021 |
Unsupervised mapping of phase diagrams of 2D systems from infinite projected entangled-pair states via deep anomaly detection K Kottmann, P Corboz, M Lewenstein, A Acín SciPost Physics 11 (2), 025, 2021 | 14 | 2021 |
Multicritical dissipative phase transitions in the anisotropic open quantum Rabi model G Lyu, K Kottmann, MB Plenio, MJ Hwang Phys. Rev. Research 6, 033075, 2024 | 4 | 2024 |
Evaluating analytic gradients of pulse programs on quantum computers K Kottmann, N Killoran https://arxiv.org/abs/2309.16756, 2023 | 2 | 2023 |
Reducing Entanglement with Physically Inspired Fermion-To-Qubit Mappings T Parella-Dilmé, K Kottmann, L Zambrano, L Mortimer, JS Kottmann, ... PRX Quantum 5 (3), 030333, 2024 | 1 | 2024 |
Investigating quantum many-body systems with tensor networks, machine learning and quantum computers K Kottmann arXiv preprint arXiv:2210.11130, 2022 | | 2022 |