{
  "id": "2111.12398",
  "version": "v2",
  "published": "2021-11-24T10:32:28.000Z",
  "updated": "2022-06-06T14:35:11.000Z",
  "title": "Adaptive-weighted tree tensor networks for disordered quantum many-body systems",
  "authors": [
    "Giovanni Ferrari",
    "Giuseppe Magnifico",
    "Simone Montangero"
  ],
  "comment": "8 pages, 6 figures. Published version",
  "journal": "Phys. Rev. B 105, 214201 (2022)",
  "doi": "10.1103/PhysRevB.105.214201",
  "categories": [
    "cond-mat.dis-nn",
    "cond-mat.quant-gas",
    "cond-mat.stat-mech",
    "cond-mat.str-el",
    "quant-ph"
  ],
  "abstract": "We introduce an adaptive-weighted tree tensor network, for the study of disordered and inhomogeneous quantum many-body systems. This ansatz is assembled on the basis of the random couplings of the physical system with a procedure that considers a tunable weight parameter to prevent completely unbalanced trees. Using this approach, we compute the ground state of the two-dimensional quantum Ising model in the presence of quenched random disorder and frustration, with lattice size up to $32 \\times 32$. We compare the results with the ones obtained using the standard homogeneous tree tensor networks and the completely self-assembled tree tensor networks, demonstrating a clear improvement of numerical precision as a function of the weight parameter, especially for large system sizes.",
  "revisions": [
    {
      "version": "v2",
      "updated": "2022-06-06T14:35:11.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "adaptive-weighted tree tensor network",
      "disordered quantum many-body systems",
      "weight parameter",
      "standard homogeneous tree tensor networks",
      "self-assembled tree tensor networks"
    ],
    "tags": [
      "journal article"
    ],
    "publication": {
      "publisher": "APS",
      "journal": "Phys. Rev. B"
    },
    "note": {
      "typesetting": "TeX",
      "pages": 8,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}