{
  "total": 18,
  "devueltos": 18,
  "total_catalogo": 74,
  "filtro": {
    "categoria": "ONML",
    "q": null,
    "limit": 100
  },
  "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": "2026-09-09",
    "como_reconstruir": "Baja https://quantumalgorithmzoo.org/, recomputa su sha256 y corre scripts/build-quantum-catalog.mjs"
  },
  "items": [
    {
      "id": "polynomial-quantum-speedups-for-constraint-satisfaction-prob",
      "nombre": "Polynomial Quantum Speedups for Constraint Satisfaction Problems",
      "categoria": "Optimization, Numerics, and Machine Learning",
      "categoria_id": "ONML",
      "problema": "Resolver problemas de satisfacción de restricciones (SAT y familia) con una mejora polinómica sobre el mejor backtracking clásico.",
      "speedup_declarado": "Polynomial",
      "declarado_por": "Quantum Algorithm Zoo",
      "fuente_url": "https://quantumalgorithmzoo.org/#ONML",
      "implementaciones": [
        {
          "nombre": "Classiq",
          "url": "https://short.classiq.io/grover"
        },
        {
          "nombre": "PennyLane",
          "url": "https://pennylane.ai/qml/demos/tutorial_QUBO"
        },
        {
          "nombre": "Qrisp (Quantum Backtracking)",
          "url": "https://qrisp.eu/reference/Algorithms/QuantumBacktrackingTree.html"
        }
      ],
      "referencias": [
        {
          "n": 133,
          "cita": "Andris Ambainis Quantum Search Algorithms. SIGACT News , 35 (2):22-35, 2004. [ arXiv:quant-ph/0504012 ]",
          "url": "http://arxiv.org/abs/quant-ph/0504012"
        },
        {
          "n": 134,
          "cita": "Nicolas J. Cerf, Lov K. Grover, and Colin P. Williams Nested quantum search and NP-hard problems. Applicable Algebra in Engineering, Communication and Computing , 10 (4-5):311-338, 2000.",
          "url": null
        },
        {
          "n": 264,
          "cita": "Ashley Montanaro Quantum walk speedup of backtracking algorithms arXiv:1509.02374 , 2015.",
          "url": "http://arxiv.org/abs/1509.02374"
        },
        {
          "n": 298,
          "cita": "Salvatore Mandra, Gian Giacomo Guerreschi, and Alan Aspuru-Guzik Faster than classical quantum algorithm for dense formulas of exact satisfiability and occupation problems arXiv:1512.00859 , 2015.",
          "url": "http://arxiv.org/abs/1512.00859"
        },
        {
          "n": 422,
          "cita": "Andris Ambainis and Martins Kokainis Quantum algorithm for tree size estimation, with applications to backtracking and 2-player games Proceedings of STOC 2017 , pg. 989-1002 [ arXiv:1704.06774 ]",
          "url": "https://arxiv.org/abs/1704.06774"
        },
        {
          "n": 423,
          "cita": "Fernando G.S L. Brandão, Richard Kueng, Daniel Stilck França Faster quantum and classical SDP approximations for quadratic binary optimization arXiv:1909.04613",
          "url": "https://arxiv.org/abs/1909.04613"
        },
        {
          "n": 492,
          "cita": "Alexander M. Dalzell, Nicola Pancotti, Earl T. Campbell, and Fernando G.S.L. Brandão Mind the gap: Achieving a super-Grover quantum speedup by jumping to the end Proceedings of STOC23 , 1131 - 1144, 2023. [ arXiv:2212.01513 ]",
          "url": "https://arxiv.org/abs/2212.01513"
        },
        {
          "n": 493,
          "cita": "M. B. Hastings A short path quantum algorithm for exact optimization Quantum , 2:78, 2018. [ arXiv:1802.10124 ]",
          "url": "https://arxiv.org/abs/1802.10124"
        }
      ],
      "n_referencias": 8,
      "remisiones": [
        {
          "ancla": "adiabatic",
          "url": "https://quantumalgorithmzoo.org/#adiabatic"
        }
      ],
      "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."
      }
    },
    {
      "id": "adiabatic-algorithms",
      "nombre": "Adiabatic Algorithms",
      "categoria": "Optimization, Numerics, and Machine Learning",
      "categoria_id": "ONML",
      "problema": "Resolver un problema partiendo de un hamiltoniano fácil y deformándolo despacio hasta uno cuyo estado fundamental codifica la solución. Es el modelo detrás del recocido cuántico.",
      "speedup_declarado": "A plausible example of superpolynomial speedup appears in [ 530 ]",
      "declarado_por": "Quantum Algorithm Zoo",
      "fuente_url": "https://quantumalgorithmzoo.org/#adiabatic",
      "implementaciones": [
        {
          "nombre": "Classiq (Linear Solver)",
          "url": "https://short.classiq.io/adiabatic_qlsp"
        }
      ],
      "referencias": [
        {
          "n": 85,
          "cita": "M. Szegedy Quantum speed-up of Markov chain based algorithms. In Proceedings of the 45th IEEE Symposium on Foundations of Computer Science , pg. 32, 2004.",
          "url": null
        },
        {
          "n": 96,
          "cita": "Edward Farhi, Jeffrey Goldstone, Sam Gutmann, and Michael Sipser Quantum computation by adiabatic evolution. arXiv:quant-ph/0001106 , 2000.",
          "url": "http://arxiv.org/abs/quant-ph/0001106"
        },
        {
          "n": 97,
          "cita": "Dorit Aharonov, Wim van Dam, Julia Kempe, Zeph Landau, Seth Lloyd, and Oded Regev Adiabatic Quantum Computation is Equivalent to Standard Quantum Computation. SIAM Journal on Computing , 37(1):166-194, 2007. [ arXiv:quant-ph/0405098 ]",
          "url": "http://arxiv.org/abs/quant-ph/0405098"
        },
        {
          "n": 98,
          "cita": "Jérémie Roland and Nicolas J. Cerf Quantum search by local adiabatic evolution. Physical Review A , 65(4):042308, 2002. [ arXiv:quant-ph/0107015 ]",
          "url": "http://arxiv.org/abs/quant-ph/0107015"
        },
        {
          "n": 176,
          "cita": "Silvano Garnerone, Paolo Zanardi, and Daniel A. Lidar Adiabatic quantum algorithm for search engine ranking. Physical Review Letters 108:230506, 2012.",
          "url": null
        },
        {
          "n": 179,
          "cita": "Boris Altshuler, Hari Krovi, and Jérémie Roland Anderson localization casts clouds over adiabatic quantum optimization. Proceedings of the National Academy of Sciences 107(28):12446-12450, 2010. [ arXiv:0912.0746 ]",
          "url": "http://arxiv.org/abs/0912.0746"
        },
        {
          "n": 180,
          "cita": "Ben Reichardt The quantum adiabatic optimization algorithm and local minima. In Proceedings of STOC 2004 , pg. 502-510. [ Erratum ].",
          "url": "http://www-bcf.usc.edu/~breichar/Correction.txt"
        },
        {
          "n": 181,
          "cita": "Edward Farhi, Jeffrey Goldstone, and Sam Gutmann Quantum adiabatic evolution algorithms versus simulated annealing. arXiv:quant-ph/0201031 , 2002.",
          "url": "http://arxiv.org/abs/quant-ph/0201031"
        },
        {
          "n": 182,
          "cita": "E. Farhi, J. Goldstone, D. Gosset, S. Gutmann, H. B. Meyer, and P. Shor Quantum adiabatic algorithms, small gaps, and different paths. Quantum Information and Computation , 11(3/4):181-214, 2011. [ arXiv:0909.4766 ]",
          "url": "http://arxiv.org/abs/0909.4766"
        },
        {
          "n": 183,
          "cita": "Sergey Bravyi, David P. DiVincenzo, Roberto I. Oliveira, and Barbara M. Terhal The Complexity of Stoquastic Local Hamiltonian Problems. Quantum Information and Computation , 8(5):361-385, 2008. [ arXiv:quant-ph/0606140 ]",
          "url": "http://arxiv.org/abs/quant-ph/0606140"
        },
        {
          "n": 184,
          "cita": "Rolando D. Somma and Sergio Boixo Spectral gap amplification. SIAM Journal on Computing , 42:593-610, 2013. [ arXiv:1110.2494 ]",
          "url": "http://arxiv.org/abs/1110.2494"
        },
        {
          "n": 185,
          "cita": "Sabine Jansen, Mary-Beth Ruskai, Ruedi Seiler Bounds for the adiabatic approximation with applications to quantum computation. Journal of Mathematical Physics , 48:102111, 2007. [ arXiv:quant-ph/0603175 ]",
          "url": "http://arxiv.org/abs/quant-ph/0603175"
        },
        {
          "n": 186,
          "cita": "E. Farhi, J. Goldstone, S. Gutmann, J. Lapan, A. Lundgren, and D. Preda A Quantum Adiabatic Evolution Algorithm Applied to Random Instances of an NP-Complete Problem. Science , 292(5516):472-475, 2001. [ arXiv:quant-ph/0104129 ]",
          "url": "http://arxiv.org/abs/quant-ph/0104129"
        },
        {
          "n": 187,
          "cita": "Edward Farhi, Jeffrey Goldstone, Sam Gutmann, and Daniel Nagaj How to make the quantum adiabatic algorithm fail. International Journal of Quantum Information , 6(3):503-516, 2008. [ arXiv:quant-ph/0512159 ]",
          "url": "http://arxiv.org/abs/quant-ph/0512159"
        },
        {
          "n": 188,
          "cita": "Edward Farhi, Jeffrey Goldstone, Sam Gutmann, and Daniel Nagaj Unstructured randomness, small gaps, and localization. Quantum Information and Computation , 11(9/10):840-854, 2011. [ arXiv:1010.0009 ]",
          "url": "http://arxiv.org/abs/1010.0009"
        },
        {
          "n": 189,
          "cita": "Edward Farhi, Jeffrey Goldstone, Sam Gutmann Quantum adiabatic evolution algorithms with different paths. arXiv:quant-ph/0208135 , 2002.",
          "url": "http://arxiv.org/abs/quant-ph/0208135"
        },
        {
          "n": 190,
          "cita": "Wim van Dam, Michele Mosca, and Umesh Vazirani How powerful is adiabatic quantum computation? In Proceedings of FOCS 2001 , pg. 279-287. arXiv:quant-ph/0206003 [See also this .]",
          "url": "http://arxiv.org/abs/quant-ph/0206003"
        },
        {
          "n": 191,
          "cita": "E. Farhi, D. Gosset, I. Hen, A. W. Sandvik, P. Shor, A. P. Young, and F. Zamponi The performance of the quantum adiabatic algorithm on random instances of two optimization problems on regular hypergraphs. Physical Review A , 86:052334, 2012. [ arXiv:1208.3757 ]",
          "url": "http://arxiv.org/abs/1208.3757"
        },
        {
          "n": 192,
          "cita": "Kristen L. Pudenz and Daniel A. Lidar Quantum adiabatic machine learning. Quantum Information Processing , 12:2027, 2013. [ arXiv:1109.0325 ]",
          "url": "http://arxiv.org/abs/1109.0325"
        },
        {
          "n": 193,
          "cita": "Frank Gaitan and Lane Clark Ramsey numbers and adiabatic quantum computing. Physical Review Letters , 108:010501, 2012. [ arXiv:1103.1345 ]",
          "url": "http://arxiv.org/abs/1103.1345"
        },
        {
          "n": 194,
          "cita": "Frank Gaitan and Lane Clark Graph isomorphism and adiabatic quantum computing. Physical Review A , 89(2):022342, 2014. [ arXiv:1304.5773 ]",
          "url": "http://arxiv.org/abs/1304.5773"
        },
        {
          "n": 195,
          "cita": "Hartmut Neven, Vasil S. Denchev, Geordie Rose, and William G. Macready Training a binary classifier with the quantum adiabatic algorithm. arXiv:0811.0416 , 2008.",
          "url": "http://arxiv.org/abs/0811.0416"
        },
        {
          "n": 198,
          "cita": "S. Morita, H. Nishimori Mathematical foundation of quantum annealing. Journal of Methematical Physics , 49(12):125210, 2008.",
          "url": null
        },
        {
          "n": 199,
          "cita": "A. B. Finnila, M. A. Gomez, C. Sebenik, C. Stenson, J. D. Doll Quantum annealing: a new method for minimizing multidimensional functions. Chemical Physics Letters , 219:343-348, 1994.",
          "url": null
        },
        {
          "n": 225,
          "cita": "Lawrence M. Ioannou and Michele Mosca Limitations on some simple adiabatic quantum algorithms International Journal of Quantum Information, 6(3):419-426, 2008. [ arXiv:quant-ph/0702241 ]",
          "url": "http://arxiv.org/abs/quant-ph/0702241"
        },
        {
          "n": 247,
          "cita": "Alexander Elgart and George A. Hagedorn A note on the switching adiabatic theorem Journal of Mathematical Physics 53(10):102202, 2012. [ arXiv:1204.2318 ]",
          "url": "http://arxiv.org/abs/1204.2318"
        },
        {
          "n": 406,
          "cita": "Andriyan Bayo Suksmono and Yuichiro Minato Finding Hadamard matrices by a quantum annealing machine Scientific Reports 9:14380, 2019. [ arXiv:1902.07890 ]",
          "url": "https://arxiv.org/abs/1902.07890"
        },
        {
          "n": 429,
          "cita": "Matthew B. Hastings The power of adiabatic quantum computation with no sign problem arXiv:2005.03791",
          "url": "https://arxiv.org/abs/2005.03791"
        },
        {
          "n": 508,
          "cita": "Bin Yan and Nikolai A. Sinitsyn Analytical solution for nonadiabatic quantum annealing to arbitrary Ising spin Hamiltonian Nature Communications , 13:2212, 2022.",
          "url": null
        },
        {
          "n": 509,
          "cita": "Tadashi Kadowaki and Hidetoshi Nishimori Quantum Annealing in the Transverse Ising Model Physical Review E , 58:5355, 1998. [ arXiv:cond-mat/9804280 ]",
          "url": "https://arxiv.org/abs/cond-mat/9804280"
        },
        {
          "n": 517,
          "cita": "Dong An and Lin Lin Quantum Linear System Solver Based on Time-optimal Adiabatic Quantum Computing and Quantum Approximate Optimization Algorithm ACM Transactions on Quantum Computing , 3(2):1–28, 2022. [ arXiv:1909.05500 ]",
          "url": "https://arxiv.org/abs/1909.05500"
        },
        {
          "n": 518,
          "cita": "Pedro C. S. Costa, Dong An, Yuval R. Sanders, Yuan Su, Ryan Babbush, and Dominic W. Berry Optimal Scaling Quantum Linear-Systems Solver via Discrete Adiabatic Theorem PRX Quantum , 3:040303, 2022. [ arXiv:2111.08152 ]",
          "url": "https://arxiv.org/abs/2111.08152"
        },
        {
          "n": 529,
          "cita": "Jiaqi Leng, Ethan Hickman, Joseph Li, and Xiaodi Wu Quantum Hamiltonian descent arXiv:2303.04171 , 2023.",
          "url": "https://arxiv.org/abs/2303.01471"
        },
        {
          "n": 530,
          "cita": "Jiaqi Leng, Yufan Zheng, and Xiaodi Wu A quantum-classical performance separation in nonconvex optimization arXiv:2311.00811 , 2023.",
          "url": "https://arxiv.org/abs/2311.00811"
        }
      ],
      "n_referencias": 34,
      "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."
      }
    },
    {
      "id": "quantum-approximate-optimization",
      "nombre": "Quantum Approximate Optimization",
      "categoria": "Optimization, Numerics, and Machine Learning",
      "categoria_id": "ONML",
      "problema": "Buscar buenas soluciones aproximadas a problemas combinatorios con un circuito parametrizado corto. Es QAOA, y es la familia sobre la que descansa casi toda la promesa comercial de optimización cuántica.",
      "speedup_declarado": "Superpolynomial",
      "declarado_por": "Quantum Algorithm Zoo",
      "fuente_url": "https://quantumalgorithmzoo.org/#ONML",
      "implementaciones": [
        {
          "nombre": "Classiq",
          "url": "https://short.classiq.io/qaoa"
        },
        {
          "nombre": "Cirq",
          "url": "https://github.com/quantumlib/Cirq/blob/main/examples/qaoa.py"
        },
        {
          "nombre": "PennyLane",
          "url": "https://pennylane.ai/qml/demos/tutorial_qaoa_intro"
        },
        {
          "nombre": "Qrisp",
          "url": "https://qrisp.eu/reference/Algorithms/qaoa/QAOA.html#qaoa"
        }
      ],
      "referencias": [
        {
          "n": 242,
          "cita": "Edward Farhi, Jeffrey Goldstone, and Sam Gutmann A quantum approximate optimization algorithm arXiv:1411.4028 , 2014.",
          "url": "http://arxiv.org/abs/1411.4028"
        },
        {
          "n": 243,
          "cita": "Edward Farhi, Jeffrey Goldstone, and Sam Gutmann A quantum approximate optimization algorithm applied to a bounded occurrence constraint problem arXiv:1412.6062 , 2014.",
          "url": "http://arxiv.org/abs/1412.6062"
        },
        {
          "n": 260,
          "cita": "Boaz Barak, Ankur Moitra, Ryan O'Donnell, Prasad Raghavendra, Oded Regev, David Steurer, Luca Trevisan, Aravindan Vijayaraghavan, David Witmer, and John Wright Beating the random assignment on constraint satisfaction problems of bounded degree arXiv:1505.03424 , 2015.",
          "url": "http://arxiv.org/abs/1505.03424"
        },
        {
          "n": 300,
          "cita": "Cedric Yen-Yu Lin and Yechao Zhu Performance of QAOA on typical instances of constraint satisfaction problems with bounded degree arXiv:1601.01744 , 2016.",
          "url": "http://arxiv.org/abs/1601.01744"
        },
        {
          "n": 301,
          "cita": "Dave Wecker, Matthew B. Hastings, and Matthias Troyer Training a quantum optimizer arXiv:1605.05370 , 2016.",
          "url": "http://arxiv.org/abs/1605.05370"
        },
        {
          "n": 302,
          "cita": "Edward Farhi and Aram W. Harrow Quantum supremacy through the quantum approximate optimization algorithm arXiv:1602.07674 , 2016.",
          "url": "http://arxiv.org/abs/1602.07674"
        },
        {
          "n": 314,
          "cita": "Z-C Yang, A. Rahmani, A. Shabani, H. Neven, and C. Chamon Optimizing variational quantum algorithms using Pontryagins's minimum principle arXiv:1607.06473 , 2016.",
          "url": "http://arxiv.org/abs/1607.06473"
        },
        {
          "n": 451,
          "cita": "R. Shaydulin, C. Li, S. Chakrabarti, M. DeCross, D. Herman, N. Kumar, J. Larson, D. Lykov, P. Minssen, Y. Sun, Y. Alexeev, J. M. Dreiling, J. P. Gaebler, T. M. Gatterman, J. A. Gerber, K. Gilmore, D. Gresh, N. Hewitt, C. V. Horst, S. Hu, J. Johansen, M. Matheny, T. Mengle, M. Mills, S. A. Moses, B. Neyenhuis, P. Siegfried, R. Yalovetzky, and M. Pistoia Evidence of scaling advantage for the quantum approximate optimization algorithm on a classically intractable problem Science Advances 10(22):eadm6761, 2024. [ arXiv:2308.02342 ]",
          "url": "https://arxiv.org/abs/2308.02342"
        },
        {
          "n": 452,
          "cita": "Joao Basso, Edward Farhi, Kunal Marwaha, Benjamin Villalonga, and Leo Zhou The Quantum Approximate Optimization Algorithm at high depth for MaxCut on large-girth regular graphs and the Sherrington-Kirkpatrick model Proceedings of TQC22 7:1-7:21, 2022. [ arXiv:2110.14206 ]",
          "url": "https://arxiv.org/abs/2110.14206"
        },
        {
          "n": 476,
          "cita": "Sami Boulebnane and Ashley Montanaro Solving boolean satisfiability problems with the quantum approximate optimization algorithm arXiv:2208.06909 , 2022.",
          "url": "https://arxiv.org/abs/2208.06909"
        },
        {
          "n": 531,
          "cita": "Edward Farhi, Sam Gutmann, Daniel Ranard, and Benjamin Villalonga Lower bounding the MaxCut of high girth 3-regular graphs using the QAOA arXiv:2503.12789 , 2023.",
          "url": "https://arxiv.org/abs/2503.12789"
        },
        {
          "n": 532,
          "cita": "Sami Boulebnane, Abid Khan, Minzhao Liu, Jeffrey Larson, Dylan Herman, Ruslan Shaydulin, and Marco Pistoia Evidence that the Quantum Approximate Optimization Algorithm Optimizes the Sherrington-Kirkpatrick Model Efficiently in the Average Case arXiv:2505.07929 , 2023.",
          "url": "https://arxiv.org/abs/2505.07929"
        }
      ],
      "n_referencias": 12,
      "remisiones": [],
      "evidencia_rosetta": {
        "medido": true,
        "recetas": [
          {
            "recipe_id": "RQ-0012",
            "nota": "Compresión de portafolio con restricciones (Finanzas)",
            "estado": "measuring"
          },
          {
            "recipe_id": "RQ-0019",
            "nota": "Ruteo de flota bajo incertidumbre (Minería)",
            "estado": "in test"
          },
          {
            "recipe_id": "RQ-0033",
            "nota": "Expansión de red eléctrica bajo estrés (Energía)",
            "estado": "measuring"
          }
        ],
        "donde": "https://rosettaquantum.com/v1/runs?recipe=RQ-0012"
      }
    },
    {
      "id": "gradient-estimation-and-learning-polynomials",
      "nombre": "Gradient Estimation and Learning Polynomials",
      "categoria": "Optimization, Numerics, and Machine Learning",
      "categoria_id": "ONML",
      "problema": "Estimar el gradiente de una función suave en un punto, o aprender los coeficientes de un polinomio, consultando un oráculo.",
      "speedup_declarado": "Polynomial",
      "declarado_por": "Quantum Algorithm Zoo",
      "fuente_url": "https://quantumalgorithmzoo.org/#gradients",
      "implementaciones": [],
      "referencias": [
        {
          "n": 20,
          "cita": "David Bulger Quantum basin hopping with gradient-based local optimisation. arXiv:quant-ph/0507193 , 2005.",
          "url": "http://arxiv.org/abs/quant-ph/0507193"
        },
        {
          "n": 61,
          "cita": "Stephen P. Jordan Fast quantum algorithm for numerical gradient estimation. Physical Review Letters , 95:050501, 2005. [ arXiv:quant-ph/0405146 ]",
          "url": "http://arxiv.org/abs/quant-ph/0405146"
        },
        {
          "n": 62,
          "cita": "Stephen P. Jordan Quantum Computation Beyond the Circuit Model . PhD thesis, Massachusetts Institute of Technology, 2008. [ arXiv:0809.2307 ]",
          "url": "http://arxiv.org/abs/0809.2307"
        },
        {
          "n": 94,
          "cita": "Andrew Yao On computing the minima of quadratic forms. In Proceedings of the 7th ACM Symposium on Theory of Computing , pages 23-26, 1975.",
          "url": null
        },
        {
          "n": 436,
          "cita": "András Gilyén, Srininvasan Arunachalam, and Nathan Wiebe Optimizing quantum optimization algorithms via faster quantum gradient computation Proceedings SODA 2019 , pp. 1425-1444 [ arXiv:1711.00465 ]",
          "url": "https://arxiv.org/abs/1711.00465"
        },
        {
          "n": 437,
          "cita": "Arjan Cornelissen Quantum gradient estimation of Gevrey functions arXiv:1909.13528 , 2019.",
          "url": "https://arxiv.org/abs/1909.13528"
        },
        {
          "n": 438,
          "cita": "Pan Gao, Keren Li, Shijie Wei, Jiancun Gao, and Guilu Long Quantum gradient algorithm for general polynomials Physical Review A 103:042403, 2021. [ arXiv:2004.11086 ]",
          "url": "https://arxiv.org/abs/2004.11086"
        },
        {
          "n": 439,
          "cita": "Yuxin Zhang and Changpeng Shao Quantum spectral method for gradient and Hessian estimation arXiv:2407.03833 , 2024.",
          "url": "https://arxiv.org/abs/2407.03833"
        }
      ],
      "n_referencias": 8,
      "remisiones": [
        {
          "ancla": "convex_optimization",
          "url": "https://quantumalgorithmzoo.org/#convex_optimization"
        }
      ],
      "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."
      }
    },
    {
      "id": "semidefinite-programming",
      "nombre": "Semidefinite Programming",
      "categoria": "Optimization, Numerics, and Machine Learning",
      "categoria_id": "ONML",
      "problema": "Optimizar una función lineal sobre matrices semidefinidas positivas sujetas a restricciones lineales. Es el caballo de batalla de la relajación 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."
      }
    },
    {
      "id": "convex-optimization",
      "nombre": "Convex Optimization",
      "categoria": "Optimization, Numerics, and Machine Learning",
      "categoria_id": "ONML",
      "problema": "Optimizar sobre cuerpos convexos y estimar sus volúmenes, con acceso al cuerpo por oráculo de pertenencia.",
      "speedup_declarado": "Polynomial",
      "declarado_por": "Quantum Algorithm Zoo",
      "fuente_url": "https://quantumalgorithmzoo.org/#convex_optimization",
      "implementaciones": [],
      "referencias": [
        {
          "n": 130,
          "cita": "Martin Rötteler Quantum algorithms to solve the hidden shift problem for quadratics and for functions of large Gowers norm. In Proceedings of MFCS 2009 , pg 663-674. [ arXiv:0911.4724 ]",
          "url": "http://arxiv.org/abs/0911.4724"
        },
        {
          "n": 146,
          "cita": "Ashley Montanaro The quantum query complexity of learning multilinear polynomials. Information Processing Letters , 112(11):438-442, 2012. [ arXiv:1105.3310 ]",
          "url": "http://arxiv.org/abs/1105.3310"
        },
        {
          "n": 147,
          "cita": "Tad Hogg Highly structured searches with quantum computers. Physical Review Letters 80: 2473, 1998.",
          "url": null
        },
        {
          "n": 148,
          "cita": "Markus Hunziker and David A. Meyer Quantum algorithms for highly structured search problems. Quantum Information Processing , Vol. 1, No. 3, pg. 321-341, 2002.",
          "url": null
        },
        {
          "n": 223,
          "cita": "David A. Meyer and James Pommersheim Single-query learning from abelian and non-abelian Hamming distance oracles arXiv:0912.0583",
          "url": "http://arxiv.org/abs/0912.0583"
        },
        {
          "n": 418,
          "cita": "Shouvanik Chakrabarti, Andrew M. Childs, Tongyang Li, and Xiaodi Wu Quantum algorithms and lower bounds for convex optimization arXiv:1809.01731",
          "url": "https://arxiv.org/abs/1809.01731"
        },
        {
          "n": 419,
          "cita": "S. Chakrabarti, A. M. Childs, S.-H. Hung, T. Li, C. Wang, and X. Wu Quantum algorithm for estimating volumes of convex bodies arXiv:1908.03903",
          "url": "https://arxiv.org/abs/1908.03903"
        },
        {
          "n": 420,
          "cita": "Joran van Apeldoorn, András Gilyén, Sander Gribling, and Ronald de Wolf Convex optimization using quantum oracles arXiv:1809.00643",
          "url": "https://arxiv.org/abs/1809.00643"
        },
        {
          "n": 461,
          "cita": "Simon Apers and Sander Gribling Quantum speedups for linear programming via interior point methods arXiv:2311.03215 , 2023.",
          "url": "https://arxiv.org/abs/2311.03215"
        },
        {
          "n": 477,
          "cita": "Ankit Garg, Robin Kothari, Praneeth Netrapalli, and Suhail Sherif No quantum speedup over gradient descent for non-smooth convex optimization arXiv:2010.01801 , 2020.",
          "url": "https://arxiv.org/abs/2010.01801"
        },
        {
          "n": 497,
          "cita": "Yanlin Chen and Ronald de Wolf Quantum algorithms and lower bounds for linear regression with norm constraints arXiv:2110.13086 , 2021.",
          "url": "https://arxiv.org/abs/2110.13086"
        }
      ],
      "n_referencias": 11,
      "remisiones": [
        {
          "ancla": "gradients",
          "url": "https://quantumalgorithmzoo.org/#gradients"
        },
        {
          "ancla": "semidefinite",
          "url": "https://quantumalgorithmzoo.org/#semidefinite"
        }
      ],
      "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."
      }
    },
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      "id": "optimization-by-decoded-quantum-interferometry",
      "nombre": "Optimization by Decoded Quantum Interferometry",
      "categoria": "Optimization, Numerics, and Machine Learning",
      "categoria_id": "ONML",
      "problema": "Reducir ciertos problemas de optimización a un problema de decodificación, y resolver ese.",
      "speedup_declarado": "Superpolynomial",
      "declarado_por": "Quantum Algorithm Zoo",
      "fuente_url": "https://quantumalgorithmzoo.org/#ONML",
      "implementaciones": [
        {
          "nombre": "Classiq",
          "url": "https://short.classiq.io/dqi"
        }
      ],
      "referencias": [
        {
          "n": 5,
          "cita": "Dorit Aharonov and Amnon Ta-Shma Adiabatic quantum state generation and statistical zero knowledge. In Proceedings of the 35th ACM Symposium on Theory of Computing , 2003. [ arXiv:quant-ph/0301023 ]",
          "url": "http://arxiv.org/abs/quant-ph/0301023"
        },
        {
          "n": 78,
          "cita": "Oded Regev Quantum computation and lattice problems. In Proceedings of the 43rd Symposium on Foundations of Computer Science , 2002. [ arXiv:cs/0304005 ]",
          "url": "http://arxiv.org/abs/cs/0304005"
        },
        {
          "n": 453,
          "cita": "Stephen P. Jordan, Noah Shutty, Mary Wootters, Adam Zalcman, Alexander Schmidhuber, Robbie King, Sergei V. Isakov, and Ryan Babbush Optimization by Decoded Quantum Interferometry Nature 646:831-836, 2025. [ arXiv:2408.08292 ].",
          "url": "https://arxiv.org/abs/2408.08292"
        },
        {
          "n": 454,
          "cita": "Alexander Schmidhuber, Ryan O'Donnell, Robin Kothari, Ryan Babbush Quartic quantum speedups for planted inference arXiv:2406.19378 , 2024.",
          "url": "https://arxiv.org/abs/2406.19378"
        },
        {
          "n": 455,
          "cita": "Takashi Yamakawa and Mark Zhandry Verifiable Quantum Advantage without Structure Journal of the ACM 71(3):1-50. [ arXiv:2204.02063 ]",
          "url": "https://arxiv.org/abs/2204.02063"
        },
        {
          "n": 534,
          "cita": "André Chailloux and Jean-Pierre Tillich Quantum advantage from soft decoders arXiv:2411.12553 , 2024.",
          "url": "https://arxiv.org/abs/2411.12553"
        },
        {
          "n": 544,
          "cita": "Daniel Cohen Hillel Optimization of Quadratic Constraints by Decoded Quantum Interferometry arXiv:2510.08061 , 2025.",
          "url": "https://arxiv.org/abs/2510.08061"
        },
        {
          "n": 545,
          "cita": "Ojas Parekh No Quantum Advantage in Decoded Quantum Interferometry for MaxCut arXiv:2509.19966 , 2025.",
          "url": "https://arxiv.org/abs/2509.19966"
        },
        {
          "n": 546,
          "cita": "Alexander Schmidhuber, Jonathan Z. Lu, Noah Shutty, Stephen Jordan, Alexander Poremba, and Yihui Quek Hamiltonian Decoded Quantum Interferometry arXiv:2510.07913 , 2025.",
          "url": "https://arxiv.org/abs/2510.07913"
        },
        {
          "n": 548,
          "cita": "André Chailloux and Jean-Pierre Tillich The Quantum Decoding Problem arXiv:2310.20651 , 2023.",
          "url": "https://arxiv.org/abs/2310.20651"
        },
        {
          "n": 549,
          "cita": "Agathe Blanvillain, André Chailloux, and Jean-Pierre Tillich The Quantum Decoding Problem : Tight Achievability Bounds and Application to Regev's Reduction arXiv:2509.24796 , 2025.",
          "url": "https://arxiv.org/abs/2509.24796"
        },
        {
          "n": 550,
          "cita": "André Chailloux and Paul Hermouet On the Quantum Equivalence between \\( \\textrm{S} | \\textrm{LWE} \\rangle \\) and ISIS arXiv:2510.06097 , 2025.",
          "url": "https://arxiv.org/abs/2510.06097"
        },
        {
          "n": 551,
          "cita": "Quentin Buzet and André Chailloux Fine-Grained Unambiguous Measurements arXiv:2510.07298 , 2025.",
          "url": "https://arxiv.org/abs/2510.07298"
        }
      ],
      "n_referencias": 13,
      "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."
      }
    },
    {
      "id": "linear-systems",
      "nombre": "Linear Systems",
      "categoria": "Optimization, Numerics, and Machine Learning",
      "categoria_id": "ONML",
      "problema": "Resolver Ax = b. Es HHL, y el asterisco importa: entrega un estado cuántico que codifica la solución, no el vector de respuesta.",
      "speedup_declarado": "Superpolynomial",
      "declarado_por": "Quantum Algorithm Zoo",
      "fuente_url": "https://quantumalgorithmzoo.org/#ONML",
      "implementaciones": [
        {
          "nombre": "Classiq (HHL)",
          "url": "https://short.classiq.io/hhl"
        },
        {
          "nombre": "Classiq (QSVT)",
          "url": "https://short.classiq.io/qsvt_inversion"
        },
        {
          "nombre": "Cirq (HHL)",
          "url": "https://github.com/quantumlib/Cirq/blob/main/examples/grover.py"
        },
        {
          "nombre": "Qrisp/Pennylane (HHL)",
          "url": "https://pennylane.ai/qml/demos/linear_equations_hhl_qrisp_catalyst"
        }
      ],
      "referencias": [
        {
          "n": 104,
          "cita": "Aram W. Harrow, Avinatan Hassidim, and Seth Lloyd Quantum algorithm for solving linear systems of equations. Physical Review Letters 15(103):150502, 2009. [ arXiv:0811.3171 ]",
          "url": "http://arxiv.org/abs/0811.3171"
        },
        {
          "n": 138,
          "cita": "Andris Ambainis Variable time amplitude amplification and a faster quantum algorithm for solving systems of linear equations. arXiv:1010.4458 , 2010.",
          "url": "http://arxiv.org/abs/1010.4458"
        },
        {
          "n": 156,
          "cita": "Dominic Berry Quantum algorithms for solving linear differential equations. J. Phys. A: Math. Theor. 47, 105301, 2014. [ arXiv:1010.2745 ].",
          "url": "http://arxiv.org/abs/1010.2745"
        },
        {
          "n": 169,
          "cita": "Nathan Wiebe, Daniel Braun, and Seth Lloyd Quantum data-fitting. Physical Review Letters 109, 050505, 2012. [ arXiv:1204.5242 ]",
          "url": "http://arxiv.org/abs/1204.5242"
        },
        {
          "n": 210,
          "cita": "Guoming Wang Quantum algorithms for approximating the effective resistances of electrical networks. arXiv:1311.1851",
          "url": "http://arxiv.org/abs/1311.1851"
        },
        {
          "n": 214,
          "cita": "Seth Lloyd, Masoud Mohseni, and Patrick Robentrost Quantum algorithms for supervised and unsupervised machine learning arXiv:1307.0411",
          "url": "http://arxiv.org/abs/1307.0411"
        },
        {
          "n": 220,
          "cita": "Amnon Ta-Shma Inverting well conditioned matrices in quantum logspace In Proceedings of STOC 2013 pg. 881-890.",
          "url": null
        },
        {
          "n": 222,
          "cita": "Seth Lloyd, Silvano Garnerone, and Paolo Zanardi Quantum algorithms for topological and geometric analysis of big data arXiv:1408.3106",
          "url": "http://arxiv.org/abs/1408.3106"
        },
        {
          "n": 246,
          "cita": "Scott Aaronson Read the fine print Nature Physics 11:291-293, 2015. [ fulltext ]",
          "url": "http://www.scottaaronson.com/papers/qml.pdf"
        },
        {
          "n": 249,
          "cita": "B. D. Clader, B. C. Jacobs, and C. R. Sprouse Preconditioned quantum linear system algorithm Phys. Rev. Lett. 110:250504, 2013. [ arXiv:1301.2340 ]",
          "url": "http://arxiv.org/abs/1301.2340"
        },
        {
          "n": 250,
          "cita": "S. Lloyd, M. Mohseni, and P. Rebentrost Quantum principal component analysis Nature Physics. 10(9):631, 2014. [ arXiv:1307.0401 ]",
          "url": "http://arxiv.org/abs/1307.0401"
        },
        {
          "n": 251,
          "cita": "Patrick Rebentrost, Masoud Mohseni, and Seth Lloyd Quantum support vector machine for big data classification Phys. Rev. Lett. 113, 130503, 2014. [ arXiv:1307.0471 ]",
          "url": "http://arxiv.org/abs/1307.0471"
        },
        {
          "n": 263,
          "cita": "Andrew M. Childs, Robin Kothari, and Rolando D. Somma Quantum linear systems algorithm with exponentially improved dependence on precision arXiv:1511.02306 , 2015.",
          "url": "http://arxiv.org/abs/1511.02306"
        },
        {
          "n": 279,
          "cita": "Bill Fefferman and Cedric Yen-Yu Lin A complete characterization of unitary quantum space arXiv:1604.01384 , 2016.",
          "url": "http://arxiv.org/abs/1604.01384"
        },
        {
          "n": 296,
          "cita": "Ashley Montanaro and Sam Pallister Quantum algorithms and the finite element method arXiv:1512.05903 , 2015.",
          "url": "http://arxiv.org/abs/1512.05903"
        },
        {
          "n": 297,
          "cita": "Lin-Chun Wan, Chao-Hua Yu, Shi-Jie Pan, Fei Gao, and Qiao-Yan Wen Quantum algorithm for the Toeplitz systems arXiv:1608.02184 , 2016.",
          "url": "http://arxiv.org/abs/1608.02184"
        },
        {
          "n": 309,
          "cita": "Iordanis Kerenidis and Anupam Prakash Quantum recommendation systems Innovations in Theoretical Computer Science (ITCS 2017) , LIPIcs, vol. 67 , pg. 1868-8969 . [ arXiv:1603.08675 ]",
          "url": "http://drops.dagstuhl.de/opus/portals/lipics/index.php?semnr=16054"
        },
        {
          "n": 369,
          "cita": "Pedro C.S. Costa, Stephen Jordan, and Aaron Ostrander Quantum algorithm for simulating the wave equation arXiv:1711.05394 , 2017.",
          "url": "https://arxiv.org/abs/1711.05394"
        },
        {
          "n": 400,
          "cita": "Ewin Tang A quantum-inspired classical algorithm for recommendation systems In Proceedings of STOC 2019 , pg. 217-228. [ arXiv:1807.04271 ]",
          "url": "https://arxiv.org/abs/1807.04271"
        },
        {
          "n": 401,
          "cita": "Ewin Tang Quantum-inspired classical algorithms for principal component analysis and supervised clustering arXiv:1811.00414 , 2018.",
          "url": "https://arxiv.org/abs/1811.00414"
        },
        {
          "n": 402,
          "cita": "L. Wossnig, Z. Zhao, and A. Prakash A quantum linear system algorithm for dense matrices Physical Review Letters vol. 120, no. 5, pg. 050502, 2018. arXiv:1704.06174 , 2017.",
          "url": "https://arxiv.org/abs/1704.06174"
        },
        {
          "n": 433,
          "cita": "András Gilyén, Yuan Su, Guang Hao Low, and Nathan Wiebe Quantum singular value transformation and beyond: exponential improvements for quantum matrix arithmetics Proceedings of STOC 2019 , pg. 193-204 [ arXiv:1806.01838 ]",
          "url": "https://arxiv.org/abs/1806.01838"
        },
        {
          "n": 494,
          "cita": "Pedro C.S. Costa, Dong An, Yuval R. Sanders, Yuan Su, Ryan Babbush, and Dominic W. Berry Optimal Scaling Quantum Linear-Systems Solver via Discrete Adiabatic Theorem PRX Quantum , 3:040303, 2022. [ arXiv:2111.08152 ]",
          "url": "https://arxiv.org/abs/2111.08152"
        },
        {
          "n": 543,
          "cita": "B. Baskaran, A. S. Rawat, A. Jayashankar, D. Chakravarti, K. Sugisaki, S. Roy, S. Mandal, D. Mukherjee, and V. S. Prasannaa Adapting the Harrow-Hassidim-Lloyd algorithm to quantum many-body theory Phys. Rev. Research , 5:043113, 2023. [ arXiv:2212.14781 ]",
          "url": "https://arxiv.org/abs/2212.14781"
        }
      ],
      "n_referencias": 24,
      "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."
      }
    },
    {
      "id": "estimating-determinants-and-other-spectral-sums",
      "nombre": "Estimating Determinants and Other Spectral Sums",
      "categoria": "Optimization, Numerics, and Machine Learning",
      "categoria_id": "ONML",
      "problema": "Estimar determinantes, trazas y otras sumas sobre el espectro de una matriz.",
      "speedup_declarado": "Superpolynomial",
      "declarado_por": "Quantum Algorithm Zoo",
      "fuente_url": "https://quantumalgorithmzoo.org/#ONML",
      "implementaciones": [],
      "referencias": [
        {
          "n": 527,
          "cita": "Alessandro Luongo and Changpeng Shao Quantum algorithms for spectral sums arXiv:2011.06475 , 2020.",
          "url": "https://arxiv.org/abs/2011.06475"
        },
        {
          "n": 528,
          "cita": "Vittorio Giovannetti, Seth Lloyd, and Lorenzo Maccone A quantum algorithm for estimating the determinant arXiv:2504.11049 , 2025.",
          "url": "https://arxiv.org/abs/2504.11049"
        }
      ],
      "n_referencias": 2,
      "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."
      }
    },
    {
      "id": "machine-learning",
      "nombre": "Machine Learning",
      "categoria": "Optimization, Numerics, and Machine Learning",
      "categoria_id": "ONML",
      "problema": "Entrada paraguas: agrupa las técnicas cuánticas propuestas para aprendizaje automático. Es también el área donde más claims cayeron por dequantización, es decir, por algoritmos clásicos que después igualaron la supuesta ventaja.",
      "speedup_declarado": "Varies",
      "declarado_por": "Quantum Algorithm Zoo",
      "fuente_url": "https://quantumalgorithmzoo.org/#ML",
      "implementaciones": [
        {
          "nombre": "Classiq (QSVM)",
          "url": "https://short.classiq.io/qsvm"
        },
        {
          "nombre": "Classiq (Autoencoder)",
          "url": "https://short.classiq.io/autoencoder"
        },
        {
          "nombre": "PennyLane",
          "url": "https://pennylane.ai/qml/demos/tutorial_variational_classifier"
        }
      ],
      "referencias": [
        {
          "n": 11,
          "cita": "Ethan Bernstein and Umesh Vazirani Quantum complexity theory. In Proceedings of the 25th ACM Symposium on the Theory of Computing , pages 11-20, 1993.",
          "url": null
        },
        {
          "n": 23,
          "cita": "A. M. Childs, L. J. Schulman, and U. V. Vazirani Quantum algorithms for hidden nonlinear structures. In Proceedings of the 48th IEEE Symposium on Foundations of Computer Science , pages 395-404, 2007. [ arXiv:0705.2784 ]",
          "url": "http://arxiv.org/abs/0705.2784"
        },
        {
          "n": 31,
          "cita": "Thomas Decker, Jan Draisma, and Pawel Wocjan Quantum algorithm for identifying hidden polynomials. Quantum Information and Computation , 9(3):215-230, 2009. [ arXiv:0706.1219 ]",
          "url": "http://arxiv.org/abs/0706.1219"
        },
        {
          "n": 104,
          "cita": "Aram W. Harrow, Avinatan Hassidim, and Seth Lloyd Quantum algorithm for solving linear systems of equations. Physical Review Letters 15(103):150502, 2009. [ arXiv:0811.3171 ]",
          "url": "http://arxiv.org/abs/0811.3171"
        },
        {
          "n": 146,
          "cita": "Ashley Montanaro The quantum query complexity of learning multilinear polynomials. Information Processing Letters , 112(11):438-442, 2012. [ arXiv:1105.3310 ]",
          "url": "http://arxiv.org/abs/1105.3310"
        },
        {
          "n": 192,
          "cita": "Kristen L. Pudenz and Daniel A. Lidar Quantum adiabatic machine learning. Quantum Information Processing , 12:2027, 2013. [ arXiv:1109.0325 ]",
          "url": "http://arxiv.org/abs/1109.0325"
        },
        {
          "n": 195,
          "cita": "Hartmut Neven, Vasil S. Denchev, Geordie Rose, and William G. Macready Training a binary classifier with the quantum adiabatic algorithm. arXiv:0811.0416 , 2008.",
          "url": "http://arxiv.org/abs/0811.0416"
        },
        {
          "n": 212,
          "cita": "Thomas Decker, Peter Høyer, Gabor Ivanyos, and Miklos Santha Polynomial time quantum algorithms for certain bivariate hidden polynomial problems arXiv:1305.1543",
          "url": "http://arxiv.org/abs/1305.1543"
        },
        {
          "n": 214,
          "cita": "Seth Lloyd, Masoud Mohseni, and Patrick Robentrost Quantum algorithms for supervised and unsupervised machine learning arXiv:1307.0411",
          "url": "http://arxiv.org/abs/1307.0411"
        },
        {
          "n": 221,
          "cita": "Nathan Wiebe, Ashish Kapoor, and Krysta Svore Quantum deep learning arXiv:1412.3489",
          "url": "http://arxiv.org/abs/1412.3489"
        },
        {
          "n": 222,
          "cita": "Seth Lloyd, Silvano Garnerone, and Paolo Zanardi Quantum algorithms for topological and geometric analysis of big data arXiv:1408.3106",
          "url": "http://arxiv.org/abs/1408.3106"
        },
        {
          "n": 224,
          "cita": "Markus Hunziker, David A. Meyer, Jihun Park, James Pommersheim, and Mitch Rothstein The geometry of quantum learning Quantum Information Processing 9:321-341, 2010. [ arXiv:quant-ph/0309059 ]",
          "url": "http://arxiv.org/abs/quant-ph/0309059"
        },
        {
          "n": 236,
          "cita": "Andrew W. Cross, Graeme Smith, and John A. Smolin Quantum learning robust to noise arXiv:1407.5088",
          "url": "http://arxiv.org/abs/1407.5088"
        },
        {
          "n": 237,
          "cita": "Aram W. Harrow and David J. Rosenbaum Uselessness for an oracle model with internal randomness Quantum Information and Computation 14(7/8):608-624, 2014 [ arXiv:1111.1462 ]",
          "url": "http://arxiv.org/abs/1111.1462"
        },
        {
          "n": 246,
          "cita": "Scott Aaronson Read the fine print Nature Physics 11:291-293, 2015. [ fulltext ]",
          "url": "http://www.scottaaronson.com/papers/qml.pdf"
        },
        {
          "n": 250,
          "cita": "S. Lloyd, M. Mohseni, and P. Rebentrost Quantum principal component analysis Nature Physics. 10(9):631, 2014. [ arXiv:1307.0401 ]",
          "url": "http://arxiv.org/abs/1307.0401"
        },
        {
          "n": 251,
          "cita": "Patrick Rebentrost, Masoud Mohseni, and Seth Lloyd Quantum support vector machine for big data classification Phys. Rev. Lett. 113, 130503, 2014. [ arXiv:1307.0471 ]",
          "url": "http://arxiv.org/abs/1307.0471"
        },
        {
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        {
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        {
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        {
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          "cita": "Srinivasan Arunachalam and Ronald de Wolf A survey of quantum learning theory arXiv:1701.06806 , 2017.",
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        {
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        {
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        },
        {
          "n": 354,
          "cita": "Alex Monràs, Gael Sentís, and Peter Wittek Inductive quantum learning: why you are doing it almost right arXiv:1605.07541 , 2016.",
          "url": "https://arxiv.org/abs/1605.07541"
        },
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          "url": "https://arxiv.org/abs/0903.0543"
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        {
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          "cita": "Ewin Tang Quantum-inspired classical algorithms for principal component analysis and supervised clustering arXiv:1811.00414 , 2018.",
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        {
          "n": 428,
          "cita": "Yunchao Liu, Srinivasan Arunachalam, and Kristan Temme A rigorous and robust quantum speed-up in supervised machine learning arXiv:2010.02174",
          "url": "https://arxiv.org/abs/2010.02174"
        },
        {
          "n": 433,
          "cita": "András Gilyén, Yuan Su, Guang Hao Low, and Nathan Wiebe Quantum singular value transformation and beyond: exponential improvements for quantum matrix arithmetics Proceedings of STOC 2019 , pg. 193-204 [ arXiv:1806.01838 ]",
          "url": "https://arxiv.org/abs/1806.01838"
        },
        {
          "n": 456,
          "cita": "Xujie Song, Tong Liu, Shengbo Eben Li, Jingliang Duan, Wenxuan Wang, and Keqiang Li Training multi-layer neural networks on Ising machine arXiv:2311.03408 .",
          "url": "https://arxiv.org/abs/2311.03408"
        },
        {
          "n": 487,
          "cita": "Sam McArdle, András Gilyén, and Mario Berta A streamlined quantum algorithm for topological data analysis with exponentially fewer qubits arXiv:2209.12887 , 2022.",
          "url": "https://arxiv.org/abs/2209.12887"
        },
        {
          "n": 488,
          "cita": "Bernardo Ameneyro, Vasileios Maroulas, and George Siopsis Quantum persistent homology Journal of Applied and Computational Topology , 1-20, 2024. [ arXiv:2202.12965 ]",
          "url": "https://arxiv.org/abs/2202.12965"
        },
        {
          "n": 490,
          "cita": "Dominic W. Berry, Yuan Su, Casper Gyurik, Robbie King, Joao Basso, Alexander Del Toro Barba, Abhishek Rajput, Nathan Wiebe, Vedran Dunjko, and Ryan Babbush Analyzing prospects for quantum advantage in topological data analysis PRX Quantum , 5:010319, 2022. [ arXiv:2209.13581 ]",
          "url": "https://arxiv.org/abs/2209.13581"
        }
      ],
      "n_referencias": 56,
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      "nombre": "Tensor Principal Component Analysis",
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      "problema": "Recuperar una señal escondida en un tensor de ruido gaussiano de muchas dimensiones.",
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        {
          "n": 424,
          "cita": "Matthew B. Hastings Classical and Quantum Algorithms for Tensor Principal Component Analysis Quantum 4:237, 2020. [ arXiv:1907.12724 ]",
          "url": "https://arxiv.org/abs/1907.12724"
        }
      ],
      "n_referencias": 1,
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      "nombre": "Solving Linear Differential Equations",
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      "categoria_id": "ONML",
      "problema": "Resolver sistemas de ecuaciones diferenciales lineales.",
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          "url": "https://short.classiq.io/time_marching"
        }
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      "referencias": [
        {
          "n": 104,
          "cita": "Aram W. Harrow, Avinatan Hassidim, and Seth Lloyd Quantum algorithm for solving linear systems of equations. Physical Review Letters 15(103):150502, 2009. [ arXiv:0811.3171 ]",
          "url": "http://arxiv.org/abs/0811.3171"
        },
        {
          "n": 156,
          "cita": "Dominic Berry Quantum algorithms for solving linear differential equations. J. Phys. A: Math. Theor. 47, 105301, 2014. [ arXiv:1010.2745 ].",
          "url": "http://arxiv.org/abs/1010.2745"
        },
        {
          "n": 245,
          "cita": "Dominic W. Berry, Andrew M. Childs, and Robin Kothari Hamiltonian simulation with nearly optimal dependence on all parameters arXiv:1501.01715 , 2015.",
          "url": "http://arxiv.org/abs/1501.01715"
        },
        {
          "n": 249,
          "cita": "B. D. Clader, B. C. Jacobs, and C. R. Sprouse Preconditioned quantum linear system algorithm Phys. Rev. Lett. 110:250504, 2013. [ arXiv:1301.2340 ]",
          "url": "http://arxiv.org/abs/1301.2340"
        },
        {
          "n": 296,
          "cita": "Ashley Montanaro and Sam Pallister Quantum algorithms and the finite element method arXiv:1512.05903 , 2015.",
          "url": "http://arxiv.org/abs/1512.05903"
        },
        {
          "n": 369,
          "cita": "Pedro C.S. Costa, Stephen Jordan, and Aaron Ostrander Quantum algorithm for simulating the wave equation arXiv:1711.05394 , 2017.",
          "url": "https://arxiv.org/abs/1711.05394"
        },
        {
          "n": 410,
          "cita": "Dominic W. Berry, Andrew M. Childs, Aaron Ostrander, and Guoming Wang Quantum algorithm for linear differential equations with exponentially improved dependence on precision Communications in Mathematical Physics , 356(3):1057-1081, 2017. [ arXiv:1701.03684 ]",
          "url": "https://arxiv.org/abs/1701.03684"
        },
        {
          "n": 412,
          "cita": "Y. Cao, A. Papageorgiou, I. Petras, J. Traub, and S. Kais Quantum algorithm and circuit design solving the Poisson equation New Journal of Physics 15(1):013021, 2013. [ arXiv:1207.2485 ]",
          "url": "https://arxiv.org/abs/1207.2485"
        },
        {
          "n": 413,
          "cita": "S. Wang, Z. Wang, W. Li, L. Fan, Z. Wei, and Y. Gu Quantum fast Poisson solver: the algorithm and modular circuit design arXiv:1910.09756 , 2019.",
          "url": "https://arxiv.org/abs/1910.09756"
        },
        {
          "n": 414,
          "cita": "A. Scherer, B. Valiron, S.-C. Mau, S. Alexander, E. van den Berg, and T. Chapuran Concrete resource analysis of the quantum linear system algorithm used to compute the electromagnetic scattering crossection of a 2D target Quantum Information Processing 16:60, 2017. [ arXiv:1505.06552 ]",
          "url": "https://arxiv.org/abs/1505.06552"
        },
        {
          "n": 415,
          "cita": "Juan Miguel Arrazola, Timjan Kalajdziavski, Christian Weedbrook, and Seth Lloyd Quantum algorithm for nonhomogeneous linear partial differential equations Physical Review A 100:032306, 2019. [ arXiv:1809.02622 ]",
          "url": "https://arxiv.org/abs/1809.02622"
        },
        {
          "n": 416,
          "cita": "Andrew Childs and Jin-Peng Liu Quantum spectral methods for differential equations arXiv:1901.00961",
          "url": "https://arxiv.org/abs/1901.00961"
        },
        {
          "n": 440,
          "cita": "Ryan Babbush, Dominic W. Berry, Robin Kothari, Rolando D. Somma, and Nathan Wiebe Exponential quantum speedup in simulating coupled classical oscillators Physical Review X 13:041041, 2024. [ arXiv:2303.13012 ]",
          "url": "https://arxiv.org/abs/2303.13012"
        },
        {
          "n": 442,
          "cita": "Andrew M. Childs, Jin-Peng Liu, and Aaron Ostrander High-precision quantum algorithms for partial differential equations Quantum 5:574, 2021. [ arXiv:2002.07868 ]",
          "url": "https://arxiv.org/abs/2002.07868"
        },
        {
          "n": 446,
          "cita": "Noah Linden, Ashley Montanaro, Changpeng Shao Quantum vs. classical algorithms for solving the heat equation Communications in Mathematical Physics 395:601, 2022. [ arXiv:2004.06516 ]",
          "url": "https://arxiv.org/abs/2004.06516"
        },
        {
          "n": 459,
          "cita": "Guang Hao Low and Yuan Su Quantum eigenvalue processing arXiv:2401.06240 , 2024.",
          "url": "https://arxiv.org/abs/2401.06240"
        },
        {
          "n": 512,
          "cita": "Rolando D. Somma, Robbie King, Robin Kothari, Thomas O'Brien, and Ryan Babbush Shadow Hamiltonian Simulation arXiv:2407.21775 , 2024.",
          "url": "https://arxiv.org/abs/2407.21775"
        },
        {
          "n": 513,
          "cita": "Maarten Stroeks, Daan Lenterman, Barbara Terhal, and Yaroslav Herasymenko Solving Free Fermion Problems on a Quantum Computer arXiv:2409.04550 , 2024.",
          "url": "https://arxiv.org/abs/2409.04550"
        },
        {
          "n": 514,
          "cita": "Alice Barthe, M. Cerezo, Andrew T. Sornborger, Martín Larocca, and Diego García-Martín Gate-Based Quantum Simulation of Gaussian Bosonic Circuits on Exponentially Many Modes Physical Review Letters , 134:070604, 2025. [ arXiv:2407.06290 ]",
          "url": "https://arxiv.org/abs/2407.06290"
        }
      ],
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        "lectura": "Rosetta no tiene ninguna corrida sellada sobre este algoritmo. Que esté catalogado no significa que lo hayamos medido ni que lo ofrezcamos."
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      "problema": "Resolver ecuaciones diferenciales no lineales, con supuestos fuertes sobre cuán no lineales pueden ser.",
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      "declarado_por": "Quantum Algorithm Zoo",
      "fuente_url": "https://quantumalgorithmzoo.org/#ONML",
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        {
          "n": 411,
          "cita": "Sarah K. Leyton and Tobias J. Osborne Quantum algorithm to solve nonlinear differential equations arXiv:0812.4423",
          "url": "https://arxiv.org/abs/0812.4423"
        },
        {
          "n": 417,
          "cita": "Alexander Engle, Graeme Smith, and Scott E. Parker A quantum algorithm for the Vlasov equation arXiv:1907.09418",
          "url": "https://arxiv.org/abs/1907.09418"
        },
        {
          "n": 426,
          "cita": "J-P Liu, H. Kolden, H. Krovi, N. Loureiro, K. Trivisa, and A. M. Childs Efficient quantum algorithm for dissipative nonlinear differential equations arXiv:2011.03185",
          "url": "https://arxiv.org/abs/2011.03185"
        },
        {
          "n": 427,
          "cita": "S. Lloyd, G. De Palma, C. Gokler, B. Kiani, Z-W Liu, M. Marvian, F. Tennie, and T. Palmer Quantum algorithm for nonlinear differential equations arXiv:2011.06571",
          "url": "https://arxiv.org/abs/2011.06571"
        },
        {
          "n": 434,
          "cita": "Dong An, Di Fang, Stephen Jordan, Jin-Peng Liu, Guang Hao Low, and Jiasu Wang Efficient quantum algorithm for nonlinear reaction-diffusion equations and energy estimation arXiv:2205.01141 , 2022.",
          "url": "https://arxiv.org/abs/2205.01141"
        },
        {
          "n": 441,
          "cita": "Hari Krovi Improved quantum algorithms for linear and nonlinear differential equations Quantum 7:913, 2023. [ arXiv:2202.01054 ]",
          "url": "https://arxiv.org/abs/2202.01054"
        },
        {
          "n": 443,
          "cita": "Dong An, Noah Linden, Jin-Peng Liu, Ashley Montanaro, Changpeng Shao, and Jiasu Wang Quantum-accelerated multilevel Monte Carlo methods for stochastic differential equations in mathematical finance Quantum 5:481, 2021. [ arXiv:2012.06283 ]",
          "url": "https://arxiv.org/abs/2012.06283"
        },
        {
          "n": 444,
          "cita": "G. Xu, A. J. Daley, P. Givi, and R. D. Somma Turbulent mixing simulation via a quantum algorithm AIAA Journal 56(2):687-699, 2018.",
          "url": null
        },
        {
          "n": 445,
          "cita": "G. Xu, A. J. Daley, P. Givi, and R. D. Somma Quantum algorithm for the computation of the reactant conversion rate in homogeneous turbulence Combustion Theory and Modelling 23(6):1090-1104, 2018.",
          "url": null
        },
        {
          "n": 447,
          "cita": "K. Kaneko, K. Miyamoto, N. Takeda, and K. Yoshino Quantum speedup of Monte Carlo integration in the directino of dimension and its application to finance Quantum Information Processing 20:185, 2021. [ arXiv:2011.02165 ]",
          "url": "https://arxiv.org/abs/2011.02165"
        },
        {
          "n": 448,
          "cita": "P. Rebentrost, B. Gupt, and T. R. Bromley Quantum computational finance: Monte Carlo pricing of financial derivatives Physical Review A 98(2):022321, 2018. [ arXiv:1805.00109 ]",
          "url": "https://arxiv.org/abs/1805.00109"
        },
        {
          "n": 449,
          "cita": "Javier Gonzalez-Conde, Ángel Rodríguez-Rozas, Enrique Solano, and Mikel Sanz Efficient Hamiltonian simulation for solving option price dynamics Physical Review Research 5:043220, 2024. [ arXiv:2101.04023 ]",
          "url": "https://arxiv.org/abs/2101.04023"
        },
        {
          "n": 450,
          "cita": "Adam Bouland, Wim van Dam, Hamed Joorati, Iordanis Kerenidis, Anupam Prakash Prospects and challenges of quantum finance arXiv:2011.06492 , 2020.",
          "url": "https://arxiv.org/abs/2011.06492"
        }
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      "categoria_id": "ONML",
      "problema": "Resolver por programación dinámica problemas del tipo camino en el hipercubo, familia que incluye al viajante.",
      "speedup_declarado": "Polynomial",
      "declarado_por": "Quantum Algorithm Zoo",
      "fuente_url": "https://quantumalgorithmzoo.org/#ONML",
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        {
          "n": 409,
          "cita": "A. Ambainis, K. Balodis, J. Iraids, M. Kokainis, K. Prusis, and J. Vihrovs Quantum speedups for exponential-time dynamic programming algorithms Proceedings of the 30th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA 19) , pg. 1783-1793, 2019. [ arXiv:1807.05209 ]",
          "url": "https://arxiv.org/abs/1807.05209"
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      "nombre": "Computing the Principal Eigenvector",
      "categoria": "Optimization, Numerics, and Machine Learning",
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      "problema": "Calcular el vector propio principal de una matriz, el núcleo de métodos tipo PageRank.",
      "speedup_declarado": "Polynomial",
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      "fuente_url": "https://quantumalgorithmzoo.org/#ONML",
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        {
          "n": 462,
          "cita": "Yanlin Chen, András Gilyén, and Ronald de Wolf A quantum speed-up for approximating the top eigenvectors of a matrix arXiv:2405.14765 , 2024.",
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      "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."
      }
    },
    {
      "id": "approximating-nash-equilibria",
      "nombre": "Approximating Nash Equilibria",
      "categoria": "Optimization, Numerics, and Machine Learning",
      "categoria_id": "ONML",
      "problema": "Aproximar equilibrios de Nash en juegos de dos jugadores.",
      "speedup_declarado": "Polynomial",
      "declarado_por": "Quantum Algorithm Zoo",
      "fuente_url": "https://quantumalgorithmzoo.org/#ONML",
      "implementaciones": [],
      "referencias": [
        {
          "n": 485,
          "cita": "Adam Bouland, Yosheb Getachew, Yujia Jin, Aaron Sidford, and Kevin Tian Quantum Speedups for zero-sum games via improved dynamic Gibbs sampling Proceedings of ICML23 , 2023. [ arXiv:2301.03763 ]",
          "url": "https://arxiv.org/abs/2301.03763"
        },
        {
          "n": 486,
          "cita": "Joran van Apeldoorn and András Gilyén Quantum algorithms for zero-sum games arXiv:1904.03180 , 2019.",
          "url": "https://arxiv.org/abs/1904.03180"
        }
      ],
      "n_referencias": 2,
      "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."
      }
    },
    {
      "id": "lattice-problems-by-filtering",
      "nombre": "Lattice Problems by Filtering",
      "categoria": "Optimization, Numerics, and Machine Learning",
      "categoria_id": "ONML",
      "problema": "Atacar problemas de retículos, que son la base de la criptografía post-cuántica estandarizada por NIST.",
      "speedup_declarado": "Exponential",
      "declarado_por": "Quantum Algorithm Zoo",
      "fuente_url": "https://quantumalgorithmzoo.org/#ONML",
      "implementaciones": [],
      "referencias": [
        {
          "n": 5,
          "cita": "Dorit Aharonov and Amnon Ta-Shma Adiabatic quantum state generation and statistical zero knowledge. In Proceedings of the 35th ACM Symposium on Theory of Computing , 2003. [ arXiv:quant-ph/0301023 ]",
          "url": "http://arxiv.org/abs/quant-ph/0301023"
        },
        {
          "n": 78,
          "cita": "Oded Regev Quantum computation and lattice problems. In Proceedings of the 43rd Symposium on Foundations of Computer Science , 2002. [ arXiv:cs/0304005 ]",
          "url": "http://arxiv.org/abs/cs/0304005"
        },
        {
          "n": 498,
          "cita": "Yilei Chen, Qipeng Liu, and Mark Zhandry Quantum algorithms for variants of average-case lattice problems via filtering Proceedings of EUROCRYPT22 , 372 - 401, 2022. [ arXiv:2108.11015 ]",
          "url": "https://arxiv.org/abs/2108.11015"
        }
      ],
      "n_referencias": 3,
      "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."
      }
    },
    {
      "id": "double-bracket-quantum-algorithms",
      "nombre": "Double-bracket quantum algorithms",
      "categoria": "Optimization, Numerics, and Machine Learning",
      "categoria_id": "ONML",
      "problema": "Familia basada en flujos de doble corchete para diagonalizar y preparar estados. La fuente declara su ventaja como desconocida.",
      "speedup_declarado": "Unknown",
      "declarado_por": "Quantum Algorithm Zoo",
      "fuente_url": "https://quantumalgorithmzoo.org/#ONML",
      "implementaciones": [
        {
          "nombre": "Numpy",
          "url": "https://github.com/marekgluza/DBQITE/blob/main/README.md"
        },
        {
          "nombre": "Qibo",
          "url": "https://github.com/qiboteam/boostvqe"
        },
        {
          "nombre": "Qrisp",
          "url": "https://qrisp.eu/reference/Algorithms/QITE.html"
        }
      ],
      "referencias": [
        {
          "n": 250,
          "cita": "S. Lloyd, M. Mohseni, and P. Rebentrost Quantum principal component analysis Nature Physics. 10(9):631, 2014. [ arXiv:1307.0401 ]",
          "url": "http://arxiv.org/abs/1307.0401"
        },
        {
          "n": 519,
          "cita": "Jeongrak Son, Marek Gluza, Ryuji Takagi, and Nelly H. Y. Ng Quantum Dynamic Programming Phys. Rev. Lett. , 134, 180602,2025. [ arXiv:2403.09187 ]",
          "url": "https://arxiv.org/abs/2403.09187"
        },
        {
          "n": 520,
          "cita": "Fuchuan Wei, Zhenhuan Liu, Guoding Liu, Zizhao Han, Xiongfeng Ma, Dong-Ling Deng, and Zhengwei Liu Simulating non-completely positive actions via exponentiation of Hermitian-preserving maps npj Quantum Information 10:134, 2024. [ arXiv:2308.07956 ]",
          "url": "https://arxiv.org/abs/2308.07956"
        },
        {
          "n": 522,
          "cita": "Marek Gluza Double-bracket quantum algorithms for diagonalization Quantum 8, 1316, 2012. [ arXiv:2206.11772 ]",
          "url": "https://arxiv.org/abs/2206.11772"
        },
        {
          "n": 524,
          "cita": "Marek Gluza, Jeongrak Son, Bi Hong Tiang, Yudai Suzuki, Zoë Holmes, and Nelly H. Y. Ng Double-bracket quantum algorithms for quantum imaginary-time evolution arXiv:2412.04554 , 2024",
          "url": "https://arxiv.org/abs/2412.04554"
        },
        {
          "n": 525,
          "cita": "René Zander, Raphael Seidel, Li Xiaoyue, and Marek Gluza Role of Riemannian geometry in double-bracket quantum imaginary-time arXiv:2504.01065 , 2025",
          "url": "https://arxiv.org/abs/2504.01065"
        }
      ],
      "n_referencias": 6,
      "remisiones": [
        {
          "ancla": "HM",
          "url": "https://quantumalgorithmzoo.org/#HM"
        },
        {
          "ancla": "RPP24",
          "url": "https://quantumalgorithmzoo.org/#RPP24"
        },
        {
          "ancla": "STS24",
          "url": "https://quantumalgorithmzoo.org/#STS24"
        }
      ],
      "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."
      }
    }
  ]
}