{
  "aviso": "speedup_declarado es lo que declara la fuente citada, NO una medición de Rosetta. Lo que Rosetta midió va en evidencia_rosetta, y para la mayoría del catálogo está vacío.",
  "procedencia": {
    "fuente": "Quantum Algorithm Zoo",
    "fuente_url": "https://quantumalgorithmzoo.org/",
    "instantanea_sha256": "dee7e76b5f19096ed329c88714744b93babf7b7d0296eb97e357b2582d16b75e",
    "generado_at": "2026-08-09"
  },
  "id": "semidefinite-programming",
  "nombre": "Semidefinite Programming",
  "categoria": "Optimization, Numerics, and Machine Learning",
  "categoria_id": "ONML",
  "problema": "Optimizar una funcion lineal sobre matrices semidefinidas positivas sujetas a restricciones lineales. Es el caballo de batalla de la relajacion convexa.",
  "speedup_declarado": "Polynomial (with some exceptions)",
  "declarado_por": "Quantum Algorithm Zoo",
  "fuente_url": "https://quantumalgorithmzoo.org/#semidefinite",
  "implementaciones": [],
  "referencias": [
    {
      "n": 121,
      "cita": "David Poulin and Pawel Wocjan Sampling from the thermal quantum Gibbs state and evaluating partition functions with a quantum computer. Physical Review Letters 103:220502, 2009. [ arXiv:0905.2199 ]",
      "url": "http://arxiv.org/abs/0905.2199"
    },
    {
      "n": 307,
      "cita": "Anirban Naryan Chowdhury and Rolando D. Somma Quantum algorithms for Gibbs sampling and hitting-time estimation arXiv:1603.02940 , 2016.",
      "url": "http://arxiv.org/abs/1603.02940"
    },
    {
      "n": 313,
      "cita": "Fernando G.S.L. Brandao and Krysta Svore Quantum speed-ups for semidefinite programming arXiv:1609.05537 , 2016.",
      "url": "http://arxiv.org/abs/1609.05537"
    },
    {
      "n": 383,
      "cita": "F.G.S.L. Brandão, A. Kalev, T. Li, C. Y.-Y. Lin, K. M. Svore, and X. Wu Quantum SDP Solvers: Large Speed-ups, Optimality, and Applications to Quantum Learning Proceedings of ICALP 2019 [ arXiv:1710.02581 ]",
      "url": "https://arxiv.org/abs/1710.02581"
    },
    {
      "n": 421,
      "cita": "Nai-Hui Chia, Andráas Gilyén, Tongyang Li, Han-Hsuan Lin, Ewin Tang, and Chunhao Wang Sampling-based sublinear low-rank matrix arithmetic framework for dequantizing quantum machine learning Proceedings of STOC 2020 , pg. 387-400 [ arXiv:1910.06151 ]",
      "url": "https://arxiv.org/abs/1910.06151"
    },
    {
      "n": 425,
      "cita": "Joran van Apeldoorn, András Gilyén, Sander Gribling, and Ronald de Wolf Quantum SDP-Solvers: Better upper and lower bounds Quantum 4:230, 2020. [ arXiv:1705.01843 ]",
      "url": "https://arxiv.org/abs/1705.01843"
    }
  ],
  "n_referencias": 6,
  "remisiones": [],
  "evidencia_rosetta": {
    "medido": false,
    "lectura": "Rosetta no tiene ninguna corrida sellada sobre este algoritmo. Que esté catalogado no significa que lo hayamos medido ni que lo ofrezcamos."
  }
}