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C. Mendl

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Preprint Aug 2026

Basis-update and Galerkin time integration in canonical matrix-product-state form

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 integration as a sequence of canonical MPS sweeps for Hamiltonians represented as matrix product operators. We show when two natural basis updates pro...

Maximilian Fröhlich, Richard M. Milbradt, Martin Eigel et al. · 0 citations
Preprint Sep 2026

Tensor Network Simulation of Dynamic Circuits

Dynamic circuits, which incorporate mid-circuit measurements and classically controlled operations, extend the expressive power of quantum programs and are central to applications such as quantum error correction, state preparation, and distributed quantum algorithms. However, their classical simulation is challenging...

Innocenzo Fulginiti, Alessandro Poggiali, C. Mendl · 0 citations
Preprint Sep 2026

DMRG using Belief Propagation

Tensor networks have attracted much attention as a powerful tool for modeling quantum many-body systems. Their contraction is a significant challenge, however, especially in highly connected networks, as memory requirements become prohibitive and the optimal contraction order is increasingly hard to find. The belief pr...

Hendrik Kühne, C. Mendl · 0 citations

qTPU: Hybrid Tensor Networks for Quantum-Classical Acceleration

The hybrid tensor network (hTN) abstraction is introduced—a unified representation capturing quantum-classical computation—realized in qTPU, an end-to-end sys-tem comprising: the qTPU programming model for declarative hybrid computation specification; the qTPU compiler for holistic hTN optimization balancing classical...

Nathaniel Tornow, Emmanouil Giortamis, Dennis Sprokholt et al. · 1 citation
#artificial intelligence Preprint Sep 2026

Fidelity-Aware Scheduling of Quantum Circuits on Multi-QPU Systems

A low-overhead fidelity-aware scheduling framework for multi-QPU systems based on a Graph Neural Network that estimates, before compilation, the expected fidelity of each circuit on each available QPU, and a tunable scheduler uses these estimates to control the trade-off between execution fidelity and parallelism.

Innocenzo Fulginiti, Antonio Tudisco, Salvatore Zammuto et al. · 0 citations

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