71 citations · 139 across the 8 of their papers we have counts for
8 papers
Protein Counterfactuals via Diffusion-Guided Latent Optimization
Weronika Kłos, Sidney Bender, Lukas Kades
Deep learning models can predict protein properties with unprecedented accuracy but rarely offer mechanistic insight or actionable guidance for engineering improved variants. When…
Searching for Yang-Lee zeros in O() models
Felipe Attanasio, Marc Bauer, Lukas Kades +1
Near the second order phase transition point, QCD with two flavours of massless quarks can be approximated by an O() model, where a symmetry breaking external field can be a…
Towards sampling complex actions
Lukas Kades, Martin Gärttner, Thomas Gasenzer +1
Path integrals with complex actions are encountered for many physical systems ranging from spin- or mass-imbalanced atomic gases and graphene to quantum chromo-dynamics at finite d…
Spiking neuromorphic chip learns entangled quantum states
Stefanie Czischek, Andreas Baumbach, Sebastian Billaudelle +8
The approximation of quantum states with artificial neural networks has gained a lot of attention during the last years. Meanwhile, analog neuromorphic chips, inspired by structura…
Towards Novel Insights in Lattice Field Theory with Explainable Machine Learning
Stefan Bluecher, Lukas Kades, Jan M. Pawlowski +2
Machine learning has the potential to aid our understanding of phase structures in lattice quantum field theories through the statistical analysis of Monte Carlo samples. Available…
Spectral Reconstruction with Deep Neural Networks
Lukas Kades, Jan M. Pawlowski, Alexander Rothkopf +5
We explore artificial neural networks as a tool for the reconstruction of spectral functions from imaginary time Green's functions, a classic ill-conditioned inverse problem. Our a…
The Discrete Langevin Machine: Bridging the Gap Between Thermodynamic and Neuromorphic Systems
Lukas Kades, Jan M. Pawlowski
A formulation of Langevin dynamics for discrete systems is derived as a class of generic stochastic processes. The dynamics simplify for a two-state system and suggest a network ar…
Characteristics of a random walk on a self-inflating support
Lukas Kades, Manuel Schrauth, Maximilian Schneider +1
Self-similar dynamical processes are characterized by a growing length scale which increases with time as , where z is the dynamical exponent. The best known exa…