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Continuous tensor network renormalization for quantum fields

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arxiv 1809.05176 v1 pith:LBQR6ZFE submitted 2018-08-31 hep-th cond-mat.str-elquant-ph

classification hep-thcond-mat.str-elquant-ph
keywords continuumfieldflownetworkrenormalizationtensorconformalcontinuous
verification ladder T0 review T1 audit T2 compute T3 formal
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abstract

On the lattice, a renormalization group (RG) flow for two-dimensional partition functions expressed as a tensor network can be obtained using the tensor network renormalization (TNR) algorithm [G. Evenbly, G. Vidal, Phys. Rev. Lett. 115 (18), 180405 (2015)]. In this work we explain how to extend TNR to field theories in the continuum. First, a short-distance length scale $1/\Lambda$ is introduced in the continuum partition function by smearing the fields. The resulting object is still defined in the continuum but has no fluctuations at distances shorter than $1/\Lambda$. An infinitesimal coarse-graining step is then generated by the combined action of a $rescaling$ operator $L$ and a $disentangling$ operator $K$ that implements a quasi-local field redefinition. As demonstrated for a free boson in two dimensions, continuous TNR exactly preserves translation and rotation symmetries and can generate a proper RG flow. Moreover, from a critical fixed point of this RG flow one can then extract the conformal data of the underlying conformal field theory.

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  1. Fixed-point tensor network for compactified boson conformal field theory

    cond-mat.str-el 2026-07 conditional novelty 7.0 of 10

    Fixed-point tensors built from open-string boundary data reproduce the closed-string spectrum of the compactified boson at generic radius and generate stable RG flows with a controllable marginal deformation.

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