Квантно машинско учење (Serbian Wikipedia)
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- Dunjko, Vedran; Taylor, Jacob M.; Briegel, Hans J. (20. 9. 2016). „Quantum-Enhanced Machine Learning”. Physical Review Letters. 117 (13): 130501. Bibcode:2016PhRvL.117m0501D. arXiv:1610.08251
. doi:10.1103/PhysRevLett.117.130501. - Krenn, Mario (1. 1. 2016). „Automated Search for new Quantum Experiments”. Physical Review Letters. 116 (9): 090405. Bibcode:2016PhRvL.116i0405K. PMID 26991161. arXiv:1509.02749 . doi:10.1103/PhysRevLett.116.090405.
- Hentschel, Alexander (1. 1. 2010). „Machine Learning for Precise Quantum Measurement”. Physical Review Letters. 104 (6). Bibcode:2010PhRvL.104f3603H. arXiv:0910.0762 . doi:10.1103/PhysRevLett.104.063603.
archive.org
- Biswas, Rupak; Jiang, Zhang; Kechezi, Kostya; Knysh, Sergey; Mandrà, Salvatore; O’Gorman, Bryan; Perdomo-Ortiz, Alejando; Pethukov, Andre; Realpe-Gómez, John; Rieffel, Eleanor; Venturelli, Davide; Vasko, Fedir; Wang, Zhihui (2016). „A NASA perspective on quantum computing: Opportunities and challenges”. Parallel Computing. doi:10.1016/j.parco.2016.11.002.
- Wan, Kwok-Ho; Dahlsten, Oscar; Kristjansson, Hler; Gardner, Robert; Kim, Myungshik (2016). „Quantum generalisation of feedforward neural networks”. Bibcode:2016arXiv161201045W. arXiv:1612.01045 .
arxiv.org
- Schuld, Maria; Sinayskiy, Ilya; Petruccione, Francesco (2014). „An introduction to quantum machine learning”. Contemporary Physics. 56 (2): 172. Bibcode:2015ConPh..56..172S. arXiv:1409.3097 . doi:10.1080/00107514.2014.964942.
- Adcock, Jeremy; Allen, Euan; Day, Matthew; Frick, Stefan; Hinchliff, Janna; Johnson, Mack; Morley-Short, Sam; Pallister, Sam; Price, Alasdair; Stanisic, Stasja (2015). „Advances in quantum machine learning”. arXiv:1512.02900 [quant-ph].
- Biamonte, Jacob; Wittek, Peter; Pancotti, Nicola; Rebentrost, Patrick; Wiebe, Nathan; Lloyd, Seth (2016). „Quantum machine learning”. arXiv:1611.09347 [quant-ph].
- Dunjko, Vedran; Taylor, Jacob M.; Briegel, Hans J. (20. 9. 2016). „Quantum-Enhanced Machine Learning”. Physical Review Letters. 117 (13): 130501. Bibcode:2016PhRvL.117m0501D. arXiv:1610.08251 . doi:10.1103/PhysRevLett.117.130501.
- Wiebe, Nathan; Kapoor, Ashish; Svore, Krysta (2014). „Quantum Algorithms for Nearest-Neighbor Methods for Supervised and Unsupervised Learning”. Quantum Information & Computation ( & 4): (20). 15 (3): 0318—0358. Bibcode:2014arXiv1401.2142W. arXiv:1401.2142 .
- Lloyd, Seth; Mohseni, Masoud; Rebentrost, Patrick (2013). „Quantum algorithms for supervised and unsupervised machine learning”. arXiv:1307.0411 [quant-ph].
- Yoo, Seokwon; Bang, Jeongho; Lee, Changhyoup; Lee, Jinhyoung (2013). „A quantum speedup in machine learning: Finding a N-bit Boolean function for a classification”. New Journal of Physics. 16 (10): 103014. Bibcode:2014NJPh...16j3014Y. arXiv:1303.6055 . doi:10.1088/1367-2630/16/10/103014.
- Bény, Cédric (14. 1. 2013). „Deep learning and the renormalization group”. arXiv:1301.3124 [quant-ph].
- Rebentrost, Patrick; Mohseni, Masoud; Lloyd, Seth (2014). „Quantum Support Vector Machine for Big Data Classification”. Physical Review Letters. 113 (13): 130503. Bibcode:2014PhRvL.113m0503R. PMID 25302877. arXiv:1307.0471 . doi:10.1103/PhysRevLett.113.130503.
- Wiebe, Nathan; Braun, Daniel; Lloyd, Seth (2012). „Quantum Algorithm for Data Fitting”. Physical Review Letters. 109 (5): 050505. Bibcode:2012PhRvL.109e0505W. PMID 23006156. arXiv:1204.5242 . doi:10.1103/PhysRevLett.109.050505.
- Schuld, Maria; Sinayskiy, Ilya; Petruccione, Francesco (2016). „Prediction by linear regression on a quantum computer”. Physical Review A. 94 (2): 022342. Bibcode:2016PhRvA..94b2342S. arXiv:1601.07823 . doi:10.1103/PhysRevA.94.022342.
- Zhao, Zhikuan; Fitzsimons, Jack K.; Fitzsimons, Joseph F. (2015). „Quantum assisted Gaussian process regression”. arXiv:1512.03929 [quant-ph].
- Harrow, Aram W.; Hassidim, Avinatan; Lloyd, Seth (2008). „Quantum algorithm for solving linear systems of equations”. Physical Review Letters. 103 (15): 150502. Bibcode:2009PhRvL.103o0502H. PMID 19905613. arXiv:0811.3171 . doi:10.1103/PhysRevLett.103.150502.
- Berry, Dominic W.; Childs, Andrew M.; Kothari, Robin (2015). Hamiltonian simulation with nearly optimal dependence on all parameters. 56th Annual Symposium on Foundations of Computer Science. IEEE. стр. 792—809. arXiv:1501.01715 . doi:10.1109/FOCS.2015.54.
- Lloyd, Seth; Mohseni, Masoud; Rebentrost, Patrick (2014). „Quantum principal component analysis”. Nature Physics. 10 (9): 631. Bibcode:2014NatPh..10..631L. arXiv:1307.0401 . doi:10.1038/nphys3029.
- Giovannetti, Vittorio; Lloyd, Seth; MacCone, Lorenzo (2008). „Quantum Random Access Memory”. Physical Review Letters. 100 (16): 160501. Bibcode:2008PhRvL.100p0501G. PMID 18518173. arXiv:0708.1879 . doi:10.1103/PhysRevLett.100.160501.
- Paparo, Giuseppe Davide; Martin-Delgado, Miguel Angel (2012). „Google in a Quantum Network”. Scientific Reports. 2: 444. Bibcode:2012NatSR...2E.444P. PMC 3370332 . PMID 22685626. arXiv:1112.2079 . doi:10.1038/srep00444.
- Paparo, Giuseppe Davide; Dunjko, Vedran; Makmal, Adi; Martin-Delgado, Miguel Angel; Briegel, Hans J. (2014). „Quantum Speedup for Active Learning Agents”. Physical Review X. 4 (3): 031002. Bibcode:2014PhRvX...4c1002P. arXiv:1401.4997 . doi:10.1103/PhysRevX.4.031002.
- Crawford, Daniel; Levit, Anna; Ghadermarzy, Navid; Oberoi, Jaspreet S.; Ronagh, Pooya (2016). „Reinforcement Learning Using Quantum Boltzmann Machines”. arXiv:1612.05695 [quant-ph].
- Briegel, Hans J.; Cuevas, Gemma De las (15. 5. 2012). „Projective simulation for artificial intelligence”. Scientific Reports (на језику: енглески). 2. Bibcode:2012NatSR...2E.400B. ISSN 2045-2322. PMC 3351754 . PMID 22590690. arXiv:1104.3787 . doi:10.1038/srep00400.
- Lamata, Lucas (2017). „Basic protocols in quantum reinforcement learning with superconducting circuits”. Scientific Reports. 7: 1609. Bibcode:2017NatSR...7.1609L. arXiv:1701.05131 . doi:10.1038/s41598-017-01711-6.
- Adachi, Steven H.; Henderson, Maxwell P. (2015). „Application of quantum annealing to training of deep neural networks”. arXiv:1510.06356 [quant-ph].
- Benedetti, Marcello; Realpe-Gómez, John; Biswas, Rupak; Perdomo-Ortiz, Alejandro (2016). „Quantum-assisted learning of graphical models with arbitrary pairwise connectivity”. arXiv:1609.02542 [quant-ph].
- Benedetti, Marcello; Realpe-Gómez, John; Biswas, Rupak; Perdomo-Ortiz, Alejandro (2016). „Estimation of effective temperatures in quantum annealers for sampling applications: A case study with possible applications in deep learning”. Physical Review A. 94 (2): 022308. Bibcode:2016PhRvA..94b2308B. arXiv:1510.07611 . doi:10.1103/PhysRevA.94.022308.
- Korenkevych, Dmytro; Xue, Yanbo; Bian, Zhengbing; Chudak, Fabian; Macready, William G.; Rolfe, Jason; Andriyash, Evgeny (2016). „Benchmarking quantum hardware for training of fully visible Boltzmann machines”. arXiv:1611.04528 [quant-ph].
- Wan, Kwok-Ho; Dahlsten, Oscar; Kristjansson, Hler; Gardner, Robert; Kim, Myungshik (2016). „Quantum generalisation of feedforward neural networks”. Bibcode:2016arXiv161201045W. arXiv:1612.01045 .
- Arunachalam, Srinivasan; de Wolf, Ronald (2017). „A Survey of Quantum Learning Theory”. arXiv:1701.06806 [quant-ph].
- Arunachalam, Srinivasan; de Wolf, Ronald (2016). „Optimal Quantum Sample Complexity of Learning Algorithms”. arXiv:1607.00932 [quant-ph].
- Monràs, Alex; Sentís, Gael; Wittek, Peter (2017). „Inductive supervised quantum learning”. Physical Review Letters. 118 (19): 190503. Bibcode:2017PhRvL.118s0503M. arXiv:1605.07541 . doi:10.1103/PhysRevLett.118.190503.
- Granade, Christopher E.; Ferrie, Christopher; Wiebe, Nathan; Cory, D. G. (3. 10. 2012). „Robust Online Hamiltonian Learning”. New Journal of Physics. 14 (10): 103013. Bibcode:2012NJPh...14j3013G. ISSN 1367-2630. arXiv:1207.1655 . doi:10.1088/1367-2630/14/10/103013.
- Wiebe, Nathan; Granade, Christopher; Ferrie, Christopher; Cory, D. G. (14. 5. 2014). „Hamiltonian Learning and Certification Using Quantum Resources”. Physical Review Letters. 112 (19): 190501. Bibcode:2014PhRvL.112s0501W. ISSN 0031-9007. PMID 24877920. arXiv:1309.0876 . doi:10.1103/PhysRevLett.112.190501.
- Wiebe, Nathan; Granade, Christopher; Ferrie, Christopher; Cory, David G. (17. 4. 2014). „Quantum Hamiltonian Learning Using Imperfect Quantum Resources”. Physical Review A. 89 (4): 042314. Bibcode:2014PhRvA..89d2314W. ISSN 1050-2947. arXiv:1311.5269 . doi:10.1103/PhysRevA.89.042314.
- Sasaki, Madahide; Carlini, Alberto; Jozsa, Richard (2001). „Quantum Template Matching”. Physical Review A. 64 (2): 022317. Bibcode:2001PhRvA..64b2317S. arXiv:quant-ph/0102020 . doi:10.1103/PhysRevA.64.022317.
- Sasaki, Masahide (2002). „Quantum learning and universal quantum matching machine”. Physical Review A. 66 (2): 022303. Bibcode:2002PhRvA..66b2303S. arXiv:quant-ph/0202173 . doi:10.1103/PhysRevA.66.022303.
- Krenn, Mario (1. 1. 2016). „Automated Search for new Quantum Experiments”. Physical Review Letters. 116 (9): 090405. Bibcode:2016PhRvL.116i0405K. PMID 26991161. arXiv:1509.02749 . doi:10.1103/PhysRevLett.116.090405.
- Carleo, Giuseppe; Troyer, Matthias (9. 2. 2017). „Solving the quantum many-body problem with artificial neural networks”. Science. 355 (6325): 602. Bibcode:2017Sci...355..602C. arXiv:1606.02318 . doi:10.1126/science.aag2302.
- Hentschel, Alexander (1. 1. 2010). „Machine Learning for Precise Quantum Measurement”. Physical Review Letters. 104 (6). Bibcode:2010PhRvL.104f3603H. arXiv:0910.0762 . doi:10.1103/PhysRevLett.104.063603.
- Sasaki, Masahide; Carlini, Alberto (6. 8. 2002). „Quantum learning and universal quantum matching machine”. Physical Review A. 66 (2). Bibcode:2002PhRvA..66b2303S. arXiv:quant-ph/0202173 . doi:10.1103/PhysRevA.66.022303.
- Bisio, Alessandro; Chiribella, Giulio; D’Ariano, Giacomo Mauro; Facchini, Stefano; Perinotti, Paolo (25. 3. 2010). „Optimal quantum learning of a unitary transformation”. Physical Review A. 81 (3). Bibcode:2010PhRvA..81c2324B. arXiv:0903.0543 . doi:10.1103/PhysRevA.81.032324.
- Neigovzen, Rodion; Neves, Jorge L.; Sollacher, Rudolf; Glaser, Steffen J. (2009). „Quantum pattern recognition with liquid-state nuclear magnetic resonance”. Physical Review A. 79 (4): 042321. Bibcode:2009PhRvA..79d2321N. arXiv:0802.1592 . doi:10.1103/PhysRevA.79.042321.
- Pfeiffer, P.; Egusquiza, I. L.; Di Ventra, M.; Sanz, M.; Solano, E. (2016). „Quantum memristors”. Scientific Reports. 6: 29507. Bibcode:2016NatSR...629507P. PMC 4933948 . PMID 27381511. arXiv:1511.02192 . doi:10.1038/srep29507.
- Salmilehto, J.; Deppe, F.; Di Ventra, M.; Sanz, M.; Solano, E. (2017). „Quantum Memristors with Superconducting Circuits”. Scientific Reports. 7: 42044. Bibcode:2017NatSR...742044S. arXiv:1603.04487 . doi:10.1038/srep42044.
- Li, Ying; Holloway, Gregory W.; Benjamin, Simon C.; Briggs, G. Andrew D.; Baugh, Jonathan; Mol, Jan A. (2016). „A simple and robust quantum memristor”. arXiv:1612.08409 [cond-mat.mes-hall].
doi.org
- Schuld, Maria; Sinayskiy, Ilya; Petruccione, Francesco (2014). „An introduction to quantum machine learning”. Contemporary Physics. 56 (2): 172. Bibcode:2015ConPh..56..172S. arXiv:1409.3097 . doi:10.1080/00107514.2014.964942.
- Aïmeur, Esma; Brassard, Gilles; Gambs, Sébastien (2006). „Machine Learning in a Quantum World”. Advances in Artificial Intelligence (на језику: енглески). Springer, Berlin, Heidelberg: 431—442. doi:10.1007/11766247_37.
- Dunjko, Vedran; Taylor, Jacob M.; Briegel, Hans J. (20. 9. 2016). „Quantum-Enhanced Machine Learning”. Physical Review Letters. 117 (13): 130501. Bibcode:2016PhRvL.117m0501D. arXiv:1610.08251 . doi:10.1103/PhysRevLett.117.130501.
- Yoo, Seokwon; Bang, Jeongho; Lee, Changhyoup; Lee, Jinhyoung (2013). „A quantum speedup in machine learning: Finding a N-bit Boolean function for a classification”. New Journal of Physics. 16 (10): 103014. Bibcode:2014NJPh...16j3014Y. arXiv:1303.6055 . doi:10.1088/1367-2630/16/10/103014.
- Rebentrost, Patrick; Mohseni, Masoud; Lloyd, Seth (2014). „Quantum Support Vector Machine for Big Data Classification”. Physical Review Letters. 113 (13): 130503. Bibcode:2014PhRvL.113m0503R. PMID 25302877. arXiv:1307.0471 . doi:10.1103/PhysRevLett.113.130503.
- Wiebe, Nathan; Braun, Daniel; Lloyd, Seth (2012). „Quantum Algorithm for Data Fitting”. Physical Review Letters. 109 (5): 050505. Bibcode:2012PhRvL.109e0505W. PMID 23006156. arXiv:1204.5242 . doi:10.1103/PhysRevLett.109.050505.
- Schuld, Maria; Sinayskiy, Ilya; Petruccione, Francesco (2016). „Prediction by linear regression on a quantum computer”. Physical Review A. 94 (2): 022342. Bibcode:2016PhRvA..94b2342S. arXiv:1601.07823 . doi:10.1103/PhysRevA.94.022342.
- Harrow, Aram W.; Hassidim, Avinatan; Lloyd, Seth (2008). „Quantum algorithm for solving linear systems of equations”. Physical Review Letters. 103 (15): 150502. Bibcode:2009PhRvL.103o0502H. PMID 19905613. arXiv:0811.3171 . doi:10.1103/PhysRevLett.103.150502.
- Berry, Dominic W.; Childs, Andrew M.; Kothari, Robin (2015). Hamiltonian simulation with nearly optimal dependence on all parameters. 56th Annual Symposium on Foundations of Computer Science. IEEE. стр. 792—809. arXiv:1501.01715 . doi:10.1109/FOCS.2015.54.
- Lloyd, Seth; Mohseni, Masoud; Rebentrost, Patrick (2014). „Quantum principal component analysis”. Nature Physics. 10 (9): 631. Bibcode:2014NatPh..10..631L. arXiv:1307.0401 . doi:10.1038/nphys3029.
- Soklakov, Andrei N.; Schack, Rüdiger (2006). „Efficient state preparation for a register of quantum bits”. Physical Review A. 73: 012307. Bibcode:2006PhRvA..73a2307S. doi:10.1103/PhysRevA.73.012307.
- Giovannetti, Vittorio; Lloyd, Seth; MacCone, Lorenzo (2008). „Quantum Random Access Memory”. Physical Review Letters. 100 (16): 160501. Bibcode:2008PhRvL.100p0501G. PMID 18518173. arXiv:0708.1879 . doi:10.1103/PhysRevLett.100.160501.
- Aaronson, Scott (2015). „Read the fine print”. Nature Physics. 11 (4): 291. Bibcode:2015NatPh..11..291A. doi:10.1038/nphys3272.
- Aïmeur, Esma; Brassard, Gilles; Gambs, Sébastien (1. 2. 2013). „Quantum speed-up for unsupervised learning”. Machine Learning (на језику: енглески). 90 (2): 261—287. ISSN 0885-6125. doi:10.1007/s10994-012-5316-5.
- Paparo, Giuseppe Davide; Martin-Delgado, Miguel Angel (2012). „Google in a Quantum Network”. Scientific Reports. 2: 444. Bibcode:2012NatSR...2E.444P. PMC 3370332 . PMID 22685626. arXiv:1112.2079 . doi:10.1038/srep00444.
- Paparo, Giuseppe Davide; Dunjko, Vedran; Makmal, Adi; Martin-Delgado, Miguel Angel; Briegel, Hans J. (2014). „Quantum Speedup for Active Learning Agents”. Physical Review X. 4 (3): 031002. Bibcode:2014PhRvX...4c1002P. arXiv:1401.4997 . doi:10.1103/PhysRevX.4.031002.
- Dong, Daoyi; Chen, Chunlin; Li, Hanxiong; Tarn, Tzyh-Jong (2008). „Quantum Reinforcement Learning”. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics). 38 (5): 1207. doi:10.1109/TSMCB.2008.925743.
- Briegel, Hans J.; Cuevas, Gemma De las (15. 5. 2012). „Projective simulation for artificial intelligence”. Scientific Reports (на језику: енглески). 2. Bibcode:2012NatSR...2E.400B. ISSN 2045-2322. PMC 3351754 . PMID 22590690. arXiv:1104.3787 . doi:10.1038/srep00400.
- Lamata, Lucas (2017). „Basic protocols in quantum reinforcement learning with superconducting circuits”. Scientific Reports. 7: 1609. Bibcode:2017NatSR...7.1609L. arXiv:1701.05131 . doi:10.1038/s41598-017-01711-6.
- Biswas, Rupak; Jiang, Zhang; Kechezi, Kostya; Knysh, Sergey; Mandrà, Salvatore; O’Gorman, Bryan; Perdomo-Ortiz, Alejando; Pethukov, Andre; Realpe-Gómez, John; Rieffel, Eleanor; Venturelli, Davide; Vasko, Fedir; Wang, Zhihui (2016). „A NASA perspective on quantum computing: Opportunities and challenges”. Parallel Computing. doi:10.1016/j.parco.2016.11.002.
- Benedetti, Marcello; Realpe-Gómez, John; Biswas, Rupak; Perdomo-Ortiz, Alejandro (2016). „Estimation of effective temperatures in quantum annealers for sampling applications: A case study with possible applications in deep learning”. Physical Review A. 94 (2): 022308. Bibcode:2016PhRvA..94b2308B. arXiv:1510.07611 . doi:10.1103/PhysRevA.94.022308.
- Servedio, Rocco A.; Gortler, Steven J. (2004). „Equivalences and Separations Between Quantum and Classical Learnability”. SIAM Journal on Computing. 33 (5): 1067—1092. doi:10.1137/S0097539704412910.
- Nader, Bshouty H.; Jeffrey, Jackson C. (1999). „Learning DNF over the Uniform Distribution Using a Quantum Example Oracle”. SIAM Journal on Computing. 28 (3): 1136—1153. doi:10.1137/S0097539795293123.
- Monràs, Alex; Sentís, Gael; Wittek, Peter (2017). „Inductive supervised quantum learning”. Physical Review Letters. 118 (19): 190503. Bibcode:2017PhRvL.118s0503M. arXiv:1605.07541 . doi:10.1103/PhysRevLett.118.190503.
- Granade, Christopher E.; Ferrie, Christopher; Wiebe, Nathan; Cory, D. G. (3. 10. 2012). „Robust Online Hamiltonian Learning”. New Journal of Physics. 14 (10): 103013. Bibcode:2012NJPh...14j3013G. ISSN 1367-2630. arXiv:1207.1655 . doi:10.1088/1367-2630/14/10/103013.
- Wiebe, Nathan; Granade, Christopher; Ferrie, Christopher; Cory, D. G. (14. 5. 2014). „Hamiltonian Learning and Certification Using Quantum Resources”. Physical Review Letters. 112 (19): 190501. Bibcode:2014PhRvL.112s0501W. ISSN 0031-9007. PMID 24877920. arXiv:1309.0876 . doi:10.1103/PhysRevLett.112.190501.
- Wiebe, Nathan; Granade, Christopher; Ferrie, Christopher; Cory, David G. (17. 4. 2014). „Quantum Hamiltonian Learning Using Imperfect Quantum Resources”. Physical Review A. 89 (4): 042314. Bibcode:2014PhRvA..89d2314W. ISSN 1050-2947. arXiv:1311.5269 . doi:10.1103/PhysRevA.89.042314.
- Sasaki, Madahide; Carlini, Alberto; Jozsa, Richard (2001). „Quantum Template Matching”. Physical Review A. 64 (2): 022317. Bibcode:2001PhRvA..64b2317S. arXiv:quant-ph/0102020 . doi:10.1103/PhysRevA.64.022317.
- Sasaki, Masahide (2002). „Quantum learning and universal quantum matching machine”. Physical Review A. 66 (2): 022303. Bibcode:2002PhRvA..66b2303S. arXiv:quant-ph/0202173 . doi:10.1103/PhysRevA.66.022303.
- Sentís, Gael; Guţă, Mădălin; Adesso, Gerardo (9. 7. 2015). „Quantum learning of coherent states”. EPJ Quantum Technology (на језику: енглески). 2 (1): 17. ISSN 2196-0763. doi:10.1140/epjqt/s40507-015-0030-4.
- Krenn, Mario (1. 1. 2016). „Automated Search for new Quantum Experiments”. Physical Review Letters. 116 (9): 090405. Bibcode:2016PhRvL.116i0405K. PMID 26991161. arXiv:1509.02749 . doi:10.1103/PhysRevLett.116.090405.
- Carleo, Giuseppe; Troyer, Matthias (9. 2. 2017). „Solving the quantum many-body problem with artificial neural networks”. Science. 355 (6325): 602. Bibcode:2017Sci...355..602C. arXiv:1606.02318 . doi:10.1126/science.aag2302.
- Hentschel, Alexander (1. 1. 2010). „Machine Learning for Precise Quantum Measurement”. Physical Review Letters. 104 (6). Bibcode:2010PhRvL.104f3603H. arXiv:0910.0762 . doi:10.1103/PhysRevLett.104.063603.
- Palittpongarnpim, Pantita; Wittek, Peter; Sanders, Barry C. (13. 9. 2016). „Single-shot adaptive measurement for quantum-enhanced metrology”. Quantum Communications and Quantum Imaging XIV. International Society for Optics and Photonics: 99800H—99800H—11. doi:10.1117/12.2237355.
- Sentís, Gael; Guţă, Mădălin; Adesso, Gerardo (9. 7. 2015). „Quantum learning of coherent states”. EPJ Quantum Technology. 2 (1). doi:10.1140/epjqt/s40507-015-0030-4.
- Sasaki, Masahide; Carlini, Alberto (6. 8. 2002). „Quantum learning and universal quantum matching machine”. Physical Review A. 66 (2). Bibcode:2002PhRvA..66b2303S. arXiv:quant-ph/0202173 . doi:10.1103/PhysRevA.66.022303.
- Bisio, Alessandro; Chiribella, Giulio; D’Ariano, Giacomo Mauro; Facchini, Stefano; Perinotti, Paolo (25. 3. 2010). „Optimal quantum learning of a unitary transformation”. Physical Review A. 81 (3). Bibcode:2010PhRvA..81c2324B. arXiv:0903.0543 . doi:10.1103/PhysRevA.81.032324.
- Neigovzen, Rodion; Neves, Jorge L.; Sollacher, Rudolf; Glaser, Steffen J. (2009). „Quantum pattern recognition with liquid-state nuclear magnetic resonance”. Physical Review A. 79 (4): 042321. Bibcode:2009PhRvA..79d2321N. arXiv:0802.1592 . doi:10.1103/PhysRevA.79.042321.
- Pfeiffer, P.; Egusquiza, I. L.; Di Ventra, M.; Sanz, M.; Solano, E. (2016). „Quantum memristors”. Scientific Reports. 6: 29507. Bibcode:2016NatSR...629507P. PMC 4933948 . PMID 27381511. arXiv:1511.02192 . doi:10.1038/srep29507.
- Salmilehto, J.; Deppe, F.; Di Ventra, M.; Sanz, M.; Solano, E. (2017). „Quantum Memristors with Superconducting Circuits”. Scientific Reports. 7: 42044. Bibcode:2017NatSR...742044S. arXiv:1603.04487 . doi:10.1038/srep42044.
googleusercontent.com
static.googleusercontent.com
- „NIPS 2009 Demonstration: Binary Classification using Hardware Implementation of Quantum Annealing” (PDF). Static.googleusercontent.com. Приступљено 26. 11. 2014.
harvard.edu
adsabs.harvard.edu
- Schuld, Maria; Sinayskiy, Ilya; Petruccione, Francesco (2014). „An introduction to quantum machine learning”. Contemporary Physics. 56 (2): 172. Bibcode:2015ConPh..56..172S. arXiv:1409.3097 . doi:10.1080/00107514.2014.964942.
- Dunjko, Vedran; Taylor, Jacob M.; Briegel, Hans J. (20. 9. 2016). „Quantum-Enhanced Machine Learning”. Physical Review Letters. 117 (13): 130501. Bibcode:2016PhRvL.117m0501D. arXiv:1610.08251 . doi:10.1103/PhysRevLett.117.130501.
- Wiebe, Nathan; Kapoor, Ashish; Svore, Krysta (2014). „Quantum Algorithms for Nearest-Neighbor Methods for Supervised and Unsupervised Learning”. Quantum Information & Computation ( & 4): (20). 15 (3): 0318—0358. Bibcode:2014arXiv1401.2142W. arXiv:1401.2142 .
- Yoo, Seokwon; Bang, Jeongho; Lee, Changhyoup; Lee, Jinhyoung (2013). „A quantum speedup in machine learning: Finding a N-bit Boolean function for a classification”. New Journal of Physics. 16 (10): 103014. Bibcode:2014NJPh...16j3014Y. arXiv:1303.6055 . doi:10.1088/1367-2630/16/10/103014.
- Rebentrost, Patrick; Mohseni, Masoud; Lloyd, Seth (2014). „Quantum Support Vector Machine for Big Data Classification”. Physical Review Letters. 113 (13): 130503. Bibcode:2014PhRvL.113m0503R. PMID 25302877. arXiv:1307.0471 . doi:10.1103/PhysRevLett.113.130503.
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