English

Measurement-induced criticality as a data-structure transition

Statistical Mechanics 2022-11-03 v2 Disordered Systems and Neural Networks Quantum Physics

Abstract

We employ unsupervised learning tools to identify different phases and their transition in quantum systems subject to the combined action of unitary evolution and stochastic measurements. Specifically, we consider principal component analysis and intrinsic dimension estimation to reveal a measurement-induced structural transition in the data space. We test our approach on a 1+1D stabilizer circuit and find the quantities of interest furnish novel order parameters defined directly in the raw data space. Our results provide a first use of unsupervised tools in dynamical quantum phase transitions.

Keywords

Cite

@article{arxiv.2101.06245,
  title  = {Measurement-induced criticality as a data-structure transition},
  author = {Xhek Turkeshi},
  journal= {arXiv preprint arXiv:2101.06245},
  year   = {2022}
}

Comments

7 pages, 5 figures

R2 v1 2026-06-23T22:12:47.822Z