Pith. sign in

REVIEW 1 cited by

Matrix Product States, Random Matrix Theory and the Principle of Maximum Entropy

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1201.6324 v1 pith:NXD3KJOL submitted 2012-01-30 quant-ph cond-mat.str-elmath-phmath.MP

Benoit Collins , Carlos E. Gonzalez-Guillen , David Perez-Garcia This is my paper · ORCID
classification quant-phcond-mat.str-elmath-phmath.MP
keywords matrixentropymaximumproductrandomstatestheorychains
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Using random matrix techniques and the theory of Matrix Product States we show that reduced density matrices of quantum spin chains have generically maximum entropy.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. No-Free-Lunch Theories for Tensor-Network Machine Learning Models

    quant-ph 2024-12 conditional novelty 6.0 of 10

    Tensor-network machine learning models (MPS and PEPS) have average generalization risk lower bounded by explicit functions of training-set size and bond dimension, formalizing no-free-lunch limits for quantum-inspired...

Pith tools