Alternating cross interpolation performs elementwise operations on tensor trains in O(χ³) time with error control, improving on the standard O(χ⁴) scaling when output ranks are controlled.
Solving the gross-pitaevskii equation with quantic tensor trains: Ground states and nonlinear dynamics
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An adaptive patching method exploits block-sparse QTT structures to reduce computational costs for tensor contractions and enables efficient evaluation of bubble diagrams and Bethe-Salpeter equations.
A tensor network algorithm computes momentum-resolved spectral functions for large non-periodic super-moiré systems by mapping tight-binding problems to solvable quantum many-body simulations using kernel polynomial methods and quantum Fourier transforms.
Tailoring tensor network algorithms to the scale hierarchy in quantics representation produces faster, more robust solvers for high-dimensional linear and eigenvalue PDE problems.
Tensor-network representation of the density matrix via Chebyshev algorithm computes real-space topological markers in C8 and C10 quasicrystals and Chern mosaics at scales of hundreds of millions of sites.
citing papers explorer
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Fast elementwise operations on tensor trains with alternating cross interpolation
Alternating cross interpolation performs elementwise operations on tensor trains in O(χ³) time with error control, improving on the standard O(χ⁴) scaling when output ranks are controlled.
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Adaptive Patching for Tensor Train Computations
An adaptive patching method exploits block-sparse QTT structures to reduce computational costs for tensor contractions and enables efficient evaluation of bubble diagrams and Bethe-Salpeter equations.
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Tensor network approach to momentum-resolved spectroscopy in non-periodic super-moir\'e systems
A tensor network algorithm computes momentum-resolved spectral functions for large non-periodic super-moiré systems by mapping tight-binding problems to solvable quantum many-body simulations using kernel polynomial methods and quantum Fourier transforms.
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Tailoring tensor network techniques to the quantics representation for highly inhomogeneous problems and few body problems
Tailoring tensor network algorithms to the scale hierarchy in quantics representation produces faster, more robust solvers for high-dimensional linear and eigenvalue PDE problems.
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Tensor network method for real-space topology in quasicrystal Chern mosaics
Tensor-network representation of the density matrix via Chebyshev algorithm computes real-space topological markers in C8 and C10 quasicrystals and Chern mosaics at scales of hundreds of millions of sites.