Publikationen von D. Hofmann

Passetti, G.; Hofmann, D.; Neitemeier, P.; Grunwald, L.; Sentef, M. A.; Kennes, D. M.: Can Neural Quantum States Learn Volume-Law Ground States? Physical Review Letters 131 (3), 036502 (2023)

Baykusheva, D. R.; Kalthoff, M.; Hofmann, D.; Claassen, M.; Kennes, D. M.; Sentef, M. A.; Mitrano, M.: Witnessing Nonequilibrium Entanglement Dynamics in a Strongly Correlated Fermionic Chain. Physical Review Letters 130 (10), 106902 (2023)

Vicentini, F.; Hofmann, D.; Szabó, A.; Wu, D.; Roth, C.; Giuliani, C.; Pescia, G.; Nys, J.; Vargas-Calderón, V.; Astrakhantsev, N. et al.; Carleo, G.: NetKet 3: Machine Learning Toolbox for Many-Body Quantum Systems. SciPost Physics Codebases 7 (2022)

Hofmann, D.; Fabiani, G.; Mentink, J. H.; Carleo, G.; Sentef, M. A.: Role of stochastic noise and generalization error in the time propagation of neural-network quantum states. SciPost Physics 12 (5), 165 (2022)

Hofmann, D.; Sentef, M. A.: Resonant laser excitation and time-domain imaging of chiral topological polariton edge states. Physical Review Research 2 (3), 033386 (2020)

Wrzosek, P.; Wohlfeld, K.; Hofmann, D.; Sowiński, T.; Sentef, M. A.: Quantum walk versus classical wave: Distinguishing ground states of quantum magnets by spacetime dynamics. Physical Review B 102 (2), 024440 (2020)

Carleo, G.; Choo, K.; Hofmann, D.; Smith, J. E. T.; Westerhout, T.; Alet, F.; Davis, E. J.; Efthymiou, S.; Glasser, I.; Lin, S.-H. et al.; Mauri, M.; Mazzola, G.; Mendl, C. B.; van Nieuwenburg, E.; O’Reilly, O.; Théveniaut, H.; Torlai, G.; Vicentini, F.; Wietek, A.: NetKet: A machine learning toolkit for many-body quantum systems. SoftwareX 10, 100311 (2019)

Hofmann, D.: Neural quantum state methods for simulating quantum many-body systems. Dissertation, 209 S., Universität Hamburg, Hamburg (2023)

Hofmann, D.: Laser Control of Topological Polaritons. Master, 75 S., Universität Hamburg, Hamburg (2018)