Tensor networks (TNs), originally developed for simulating many-body quantum systems, provide a systematic framework for approximating high-dimensional fields. This is achieved by factorizing the field into interconnected tensors with small bond dimensions, thereby restricting the correlations captured across field bip...
M. Esmaeili, Hirad Alipanah, Robert Pinkston et al.· 1 citation
The results constitute, to the knowledge, the first experimental realization of nonlinear time propagation on a quantum processor, extending quantum simulation beyond predominantly linear settings and establishing a route toward quantum computation for nonlinear continuum dynamics.
J. D. da Costa Jesus, A. Setty, T. Calarco et al.· 2 citations
This work presents a hybrid quantum-classical tensor network algorithm that addresses this bottleneck by combining variational time-stepping with quantum tensor programming to efficiently compile operators and time-dependent fields into quantum circuits.
Pia Siegl, Nis-Luca van Hülst, Maximilian Mandelt Buxad'e et al.· 0 citations
This paper employs a hybrid quantum neural network (QNN) on a dataset on cloud microphysics, containing processes for phase transitions of water in the atmosphere and its related temperature changes, which are highly relevant for accurate climate predictions and projections.
Felix Herbort, Ellen Sarauer, Daniel Ohl de Mello et al.· 0 citations
Tensor networks are powerful formats for compressing large-scale data. However, their application to general data processing has been limited by the difficulty of performing nonlinear operations. Here, we introduce iterative tensor network transformations (ITNTs), a general algorithmic framework for the element-wise ev...
Xiao Wang, Tomohiro Hashizume, Pia Siegl et al.· 2 citations
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