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Entanglement renormalization

2 Pith papers cite this work. Polarity classification is still indexing.

2 Pith papers citing it
abstract

In the context of real-space renormalization group methods, we propose a novel scheme for quantum systems defined on a D-dimensional lattice. It is based on a coarse-graining transformation that attempts to reduce the amount of entanglement of a block of lattice sites before truncating its Hilbert space. Numerical simulations involving the ground state of a 1D system at criticality show that the resulting coarse-grained site requires a Hilbert space dimension that does not grow with successive rescaling transformations. As a result we can address, in a quasi-exact way, tens of thousands of quantum spins with a computational effort that scales logarithmically in the system's size. The calculations unveil that ground state entanglement in extended quantum systems is organized in layers corresponding to different length scales. At a quantum critical point, each rellevant length scale makes an equivalent contribution to the entanglement of a block with the rest of the system.

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years

2026 1 2025 1

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UNVERDICTED 2

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representative citing papers

Symmetry-Resolved Entanglement Entropy from Heat Kernels

hep-th · 2025-11-03 · unverdicted · novelty 7.0

An improved heat kernel framework with phase-factor reconstruction computes symmetry-resolved entanglement entropy for charged systems and derives a cMERA flow equation that agrees with CFT and holographic calculations.

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Showing 2 of 2 citing papers.

  • Symmetry-Resolved Entanglement Entropy from Heat Kernels hep-th · 2025-11-03 · unverdicted · none · ref 18 · internal anchor

    An improved heat kernel framework with phase-factor reconstruction computes symmetry-resolved entanglement entropy for charged systems and derives a cMERA flow equation that agrees with CFT and holographic calculations.

  • Quantum Annealing: Optimisation, Sampling, and Many-Body Dynamics quant-ph · 2026-05-07 · unverdicted · none · ref 76

    Quantum annealing is described as a heuristic for discrete optimization and sampling that also serves as a platform for studying non-equilibrium many-body quantum dynamics with programmable spin systems.