{
  "id": "2407.05978",
  "version": "v1",
  "published": "2024-07-08T14:21:36.000Z",
  "updated": "2024-07-08T14:21:36.000Z",
  "title": "Zero-temperature Monte Carlo simulations of two-dimensional quantum spin glasses guided by neural network states",
  "authors": [
    "L. Brodoloni",
    "S. Pilati"
  ],
  "comment": "10 pages, 9 figures",
  "categories": [
    "cond-mat.dis-nn",
    "cond-mat.stat-mech",
    "physics.comp-ph"
  ],
  "abstract": "A continuous-time projection quantum Monte Carlo algorithm is employed to simulate the ground state of a short-range quantum spin-glass model, namely, the two-dimensional Edwards-Anderson Hamiltonian with transverse field, featuring Gaussian nearest-neighbor couplings. We numerically demonstrate that guiding wave functions based on self-learned neural networks suppress the population control bias below modest statistical uncertainties, at least up to a hundred spins. By projecting a two-fold replicated Hamiltonian, the spin overlap is determined. A finite-size scaling analysis is performed to estimate the critical transverse field where the spin-glass transition occurs, as well as the critical exponents of the correlation length and the spin-glass susceptibility. For the latter two, good agreement is found with recent estimates from the literature for different random couplings. We also address the spin-overlap distribution within the spin-glass phase, finding that, for the workable system sizes, it displays a non-trivial double-peak shape with large weight at zero overlap.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2024-07-08T14:21:36.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "zero-temperature monte carlo simulations",
      "two-dimensional quantum spin glasses",
      "neural network states",
      "projection quantum monte carlo",
      "quantum monte carlo algorithm"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 10,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}