{
  "id": "2501.17837",
  "version": "v1",
  "published": "2025-01-29T18:35:40.000Z",
  "updated": "2025-01-29T18:35:40.000Z",
  "title": "Distinguishing Ordered Phases using Machine Learning and Classical Shadows",
  "authors": [
    "Leandro Morais",
    "Tiago Pernambuco",
    "Rodrigo G. Pereira",
    "Askery Canabarro",
    "Diogo O. Soares-Pinto",
    "Rafael Chaves"
  ],
  "comment": "12 pages, 16 figures",
  "categories": [
    "quant-ph"
  ],
  "abstract": "Classifying phase transitions is a fundamental and complex challenge in condensed matter physics. This work proposes a framework for identifying quantum phase transitions by combining classical shadows with unsupervised machine learning. We use the axial next-nearest neighbor Ising model as our benchmark and extend the analysis to the Kitaev-Heisenberg model on a two-leg ladder. Even with few qubits, we can effectively distinguish between the different phases of the Hamiltonian models. Moreover, given that we only rely on two-point correlator functions, the classical shadows protocol enables the cost of the analysis to scale logarithmically with the number of qubits, making our approach a scalable and efficient way to study phase transitions in many-body systems.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2025-01-29T18:35:40.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "distinguishing ordered phases",
      "machine learning",
      "axial next-nearest neighbor ising model",
      "quantum phase transitions",
      "two-point correlator functions"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 12,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}