{
  "model": "J1J2",
  "lattice": "square",
  "n_sites": 256,
  "boundary": "P",
  "params": {
    "J2": 0.5
  },
  "instance_id": "J1J2/square_256_P_0.5",
  "rows": [
    {
      "energy": -508.6374912,
      "sigma": 0.0008192,
      "energy_variance": null,
      "dof": 256,
      "einf": 0,
      "v_score": null,
      "method": "ResNet2 (64 conv layers), MinSR",
      "bound_type": "variational",
      "bound_type_reason": "assigned from the source text during verification",
      "reference": "Chen & Heyl, Nat. Phys. 20, 1476 (2024), arXiv:2302.01941",
      "peer_reviewed": true,
      "source": "literature-2025-26",
      "provenance": "primary",
      "verified": {
        "checked_on": "2026-09-10",
        "method": "source text/table parsed locally (arXiv HTML or pypdf), no LLM transcription",
        "reported_as": "-0.4967163 (+/- 8e-7) per site in S.S units",
        "note": "PDF text: \"our approach yields the best variational energy E/N = -0.4967163(8) ... on such a large lattice\" (16x16). No VarBench instance existed for this size.",
        "secondary_of": null
      }
    },
    {
      "energy": -508.839936,
      "sigma": 0.000512,
      "energy_variance": null,
      "dof": 256,
      "einf": 0,
      "v_score": null,
      "method": "CNN-MPS",
      "bound_type": "variational",
      "bound_type_reason": "variational ansatz; energy is a strict upper bound (assigned during source verification)",
      "reference": "arXiv:2603.14425, Disentangling Tensor Network States with Deep Neural Networks",
      "peer_reviewed": false,
      "source": "sweep-2026-09-13",
      "provenance": "primary",
      "verified": {
        "checked_on": "2026-09-13",
        "method": "arXiv HTML parsed locally, no LLM transcription",
        "reported_as": "-0.496914 (+/- 5e-7) per site in S.S units",
        "note": "Read from Table 1 of arXiv:2603.14425 (square-lattice J1-J2 at J2/J1=0.5, PBC, E per site in S.S units), parsed from the arXiv HTML. Claim: \"For L=16, CNN-MPS yields the lowest variational energy, -0.4969140(5), compared with the previously best reported value -0.4967163(8)\".",
        "secondary_of": null
      }
    },
    {
      "energy": -508.708864,
      "sigma": 0.001024,
      "energy_variance": null,
      "dof": 256,
      "einf": 0,
      "v_score": null,
      "method": "T-MPS",
      "bound_type": "variational",
      "bound_type_reason": "variational ansatz; energy is a strict upper bound (assigned during source verification)",
      "reference": "arXiv:2603.14425, Disentangling Tensor Network States with Deep Neural Networks",
      "peer_reviewed": false,
      "source": "sweep-2026-09-13",
      "provenance": "primary",
      "verified": {
        "checked_on": "2026-09-13",
        "method": "arXiv HTML parsed locally, no LLM transcription",
        "reported_as": "-0.496786 (+/- 0.000001) per site in S.S units",
        "note": "Read from Table 1 of arXiv:2603.14425 (square-lattice J1-J2 at J2/J1=0.5, PBC, E per site in S.S units), parsed from the arXiv HTML.",
        "secondary_of": null
      }
    }
  ],
  "url": "https://qmbl.org/i/J1J2/square_256_P_0.5/",
  "per_site_divisor": 1024,
  "per_site_label": "E/N (S.S)",
  "record": {
    "energy": -508.839936,
    "sigma": 0.000512,
    "method": "CNN-MPS",
    "reference": "arXiv:2603.14425, Disentangling Tensor Network States with Deep Neural Networks",
    "energy_per_site": -0.496914
  },
  "no_record_reason": null
}