4 citations · 4 across the 9 of their papers we have counts for
4 papers · 2 filters
Scalable Lindblad Noise Learning via Stochastic Tensor-Network Simulation
Alejandro R. Ramos Ramos, Maximilian Fröhlich, Aaron Sander +4
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…