6 papers · 1 filter
Scalable Lindblad Noise Learning via Stochastic Tensor-Network Simulation
Alejandro R. Ramos Ramos, Maximilian Fröhlich, Aaron Sander +5
Learning dissipation rates in large-scale open quantum systems is a major obstacle for near-term quantum technologies, as existing Lindblad estimation methods are typically limited…
Basis-update and Galerkin time integration in canonical matrix-product-state form
Maximilian Fröhlich, Richard M. Milbradt, Martin Eigel +3
Matrix product state algorithms must enlarge their bond spaces as entanglement grows and compress them to control cost. We formulate basis-update and Galerkin (BUG) time integratio…
Noisy quantum circuit simulation with the tensor jump method
Maximilian Fröhlich, Aaron Sander, Martin Eigel +2
Classical simulation of noisy quantum circuits is essential for validating algorithms, benchmarking hardware, and assessing error-mitigation strategies, but remains limited by the…
Computational regimes in matrix-product-state-based quantum trajectory simulations
Aaron Sander, Simon Cichy, Martin Eigel +4
Efficient simulation of open quantum systems is central to modeling noisy quantum hardware and many-body dynamics. In trajectory-based tensor network methods, cost is often associa…
Anderson Mixing in Bures Wasserstein Space of Gaussian Measures
Vitalii Aksenov, Martin Eigel, Mathias Oster
Various statistical tasks, including sampling or computing Wasserstein barycenters, can be reformulated as fixed-point problems for operators on probability distributions. Accelera…
A tensor network formalism for neuro-symbolic AI
Alex Goessmann, Janina Schütte, Maximilian Fröhlich +1
The unification of neural and symbolic approaches to artificial intelligence remains a central open challenge. In this work, we introduce a tensor network formalism, which captures…