Global InformationQuantum machine learning information
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Quantum machine learning is the integration of quantum algorithms within machine learning programs.[1][2][3][4][5][6][7][8]
The most common use of the term refers to machine learning algorithms for the analysis of classical data executed on a quantum computer, i.e. quantum-enhanced machine learning.[9][10][11] While machine learning algorithms are used to compute immense quantities of data, quantum machine learning utilizes qubits and quantum operations or specialized quantum systems to improve computational speed and data storage done by algorithms in a program.[12] This includes hybrid methods that involve both classical and quantum processing, where computationally difficult subroutines are outsourced to a quantum device.[13][14][15] These routines can be more complex in nature and executed faster on a quantum computer.[7] Furthermore, quantum algorithms can be used to analyze quantum states instead of classical data.[16][17]
Beyond quantum computing, the term "quantum machine learning" is also associated with classical machine learning methods applied to data generated from quantum experiments (i.e. machine learning of quantum systems), such as learning the phase transitions of a quantum system[18][19] or creating new quantum experiments.[20][21][22]
Quantum machine learning also extends to a branch of research that explores methodological and structural similarities between certain physical systems and learning systems, in particular neural networks. For example, some mathematical and numerical techniques from quantum physics are applicable to classical deep learning and vice versa.[23][24][25]
Furthermore, researchers investigate more abstract notions of learning theory with respect to quantum information, sometimes referred to as "quantum learning theory".[26][27]
- ^ Ventura, Dan (2000). "Quantum Associative Memory". Information Sciences. 124 (1–4): 273–296. arXiv:quant-ph/9807053. doi:10.1016/S0020-0255(99)00101-2. S2CID 7232952.
- ^ Trugenberger, Carlo A. (2001). "Probabilistic Quantum Memories". Physical Review Letters. 87 (6): 067901. arXiv:quant-ph/0012100. Bibcode:2001PhRvL..87f7901T. doi:10.1103/PhysRevLett.87.067901. PMID 11497863. S2CID 23325931.
- ^ Trugenberger, Carlo A. (2002). "Quantum Pattern Recognition". Quantum Information Processing. 1 (6): 471–493. doi:10.1023/A:1024022632303. S2CID 1928001.
- ^ Trugenberger, C. A. (2002-12-19). "Phase Transitions in Quantum Pattern Recognition". Physical Review Letters. 89 (27): 277903. arXiv:quant-ph/0204115. Bibcode:2002PhRvL..89A7903T. doi:10.1103/physrevlett.89.277903. ISSN 0031-9007. PMID 12513243. S2CID 33065081.
- ^ Biamonte, Jacob; Wittek, Peter; Nicola, Pancotti; Rebentrost, Patrick; Wiebe, Nathan; Lloyd, Seth (2017). "Quantum machine learning". Nature. 549 (7671): 195–202. arXiv:1611.09347. Bibcode:2017Natur.549..195B. doi:10.1038/nature23474. PMID 28905917. S2CID 64536201.
- ^ Schuld, Maria; Petruccione, Francesco (2018). Supervised Learning with Quantum Computers. Quantum Science and Technology. Bibcode:2018slqc.book.....S. doi:10.1007/978-3-319-96424-9. ISBN 978-3-319-96423-2.
- ^ a b Schuld, Maria; Sinayskiy, Ilya; Petruccione, Francesco (2014). "An introduction to quantum machine learning". Contemporary Physics. 56 (2): 172–185. arXiv:1409.3097. Bibcode:2015ConPh..56..172S. CiteSeerX 10.1.1.740.5622. doi:10.1080/00107514.2014.964942. S2CID 119263556.
- ^ Wittek, Peter (2014). Quantum Machine Learning: What Quantum Computing Means to Data Mining. Academic Press. ISBN 978-0-12-800953-6.
- ^ Wiebe, Nathan; Kapoor, Ashish; Svore, Krysta (2014). "Quantum Algorithms for Nearest-Neighbor Methods for Supervised and Unsupervised Learning". Quantum Information & Computation. 15 (3): 0318–0358. 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 (2014). "A quantum speedup in machine learning: Finding a N-bit Boolean function for a classification". New Journal of Physics. 16 (10): 103014. arXiv:1303.6055. Bibcode:2014NJPh...16j3014Y. doi:10.1088/1367-2630/16/10/103014. S2CID 4956424.
- ^ Schuld, Maria; Sinayskiy, Ilya; Petruccione, Francesco (2014-10-15). "An introduction to quantum machine learning". Contemporary Physics. 56 (2): 172–185. arXiv:1409.3097. Bibcode:2015ConPh..56..172S. CiteSeerX 10.1.1.740.5622. doi:10.1080/00107514.2014.964942. ISSN 0010-7514. S2CID 119263556.
- ^ Benedetti, Marcello; Realpe-Gómez, John; Biswas, Rupak; Perdomo-Ortiz, Alejandro (2017-11-30). "Quantum-Assisted Learning of Hardware-Embedded Probabilistic Graphical Models". Physical Review X. 7 (4): 041052. arXiv:1609.02542. Bibcode:2017PhRvX...7d1052B. doi:10.1103/PhysRevX.7.041052. ISSN 2160-3308. S2CID 55331519.
- ^ Farhi, Edward; Neven, Hartmut (2018-02-16). "Classification with Quantum Neural Networks on Near Term Processors". arXiv:1802.06002 [quant-ph].
- ^ Schuld, Maria; Bocharov, Alex; Svore, Krysta; Wiebe, Nathan (2020). "Circuit-centric quantum classifiers". Physical Review A. 101 (3): 032308. arXiv:1804.00633. Bibcode:2020PhRvA.101c2308S. doi:10.1103/PhysRevA.101.032308. S2CID 49577148.
- ^ Yu, Shang; Albarran-Arriagada, F.; Retamal, J. C.; Wang, Yi-Tao; Liu, Wei; Ke, Zhi-Jin; Meng, Yu; Li, Zhi-Peng; Tang, Jian-Shun (2018-08-28). "Reconstruction of a Photonic Qubit State with Quantum Reinforcement Learning". Advanced Quantum Technologies. 2 (7–8): 1800074. arXiv:1808.09241. doi:10.1002/qute.201800074. S2CID 85529734.
- ^ Ghosh, Sanjib; Opala, A.; Matuszewski, M.; Paterek, T.; Liew, Timothy C. H. (2019). "Quantum reservoir processing". npj Quantum Information. 5 (35): 35. arXiv:1811.10335. Bibcode:2019npjQI...5...35G. doi:10.1038/s41534-019-0149-8. S2CID 119197635.
- ^ Broecker, Peter; Assaad, Fakher F.; Trebst, Simon (2017-07-03). "Quantum phase recognition via unsupervised machine learning". arXiv:1707.00663 [cond-mat.str-el].
- ^ Huembeli, Patrick; Dauphin, Alexandre; Wittek, Peter (2018). "Identifying Quantum Phase Transitions with Adversarial Neural Networks". Physical Review B. 97 (13): 134109. arXiv:1710.08382. Bibcode:2018PhRvB..97m4109H. doi:10.1103/PhysRevB.97.134109. ISSN 2469-9950. S2CID 125593239.
- ^ Krenn, Mario (2016-01-01). "Automated Search for new Quantum Experiments". Physical Review Letters. 116 (9): 090405. arXiv:1509.02749. Bibcode:2016PhRvL.116i0405K. doi:10.1103/PhysRevLett.116.090405. PMID 26991161. S2CID 20182586.
- ^ Knott, Paul (2016-03-22). "A search algorithm for quantum state engineering and metrology". New Journal of Physics. 18 (7): 073033. arXiv:1511.05327. Bibcode:2016NJPh...18g3033K. doi:10.1088/1367-2630/18/7/073033. S2CID 2721958.
- ^ Dunjko, Vedran; Briegel, Hans J (2018-06-19). "Machine learning & artificial intelligence in the quantum domain: a review of recent progress". Reports on Progress in Physics. 81 (7): 074001. arXiv:1709.02779. Bibcode:2018RPPh...81g4001D. doi:10.1088/1361-6633/aab406. hdl:1887/71084. ISSN 0034-4885. PMID 29504942. S2CID 3681629.
- ^ Huggins, William; Patel, Piyush; Whaley, K. Birgitta; Stoudenmire, E. Miles (2018-03-30). "Towards Quantum Machine Learning with Tensor Networks". Quantum Science and Technology. 4 (2): 024001. arXiv:1803.11537. doi:10.1088/2058-9565/aaea94. S2CID 4531946.
- ^ Carleo, Giuseppe; Nomura, Yusuke; Imada, Masatoshi (2018-02-26). "Constructing exact representations of quantum many-body systems with deep neural networks". Nature Communications. 9 (1): 5322. arXiv:1802.09558. Bibcode:2018NatCo...9.5322C. doi:10.1038/s41467-018-07520-3. PMC 6294148. PMID 30552316.
- ^ Bény, Cédric (2013-01-14). "Deep learning and the renormalization group". arXiv:1301.3124 [quant-ph].
- ^ Arunachalam, Srinivasan; de Wolf, Ronald (2017-01-24). "A Survey of Quantum Learning Theory". arXiv:1701.06806 [quant-ph].
- ^ Sergioli, Giuseppe; Giuntini, Roberto; Freytes, Hector (2019-05-09). "A new Quantum approach to binary classification". PLOS ONE. 14 (5): e0216224. Bibcode:2019PLoSO..1416224S. doi:10.1371/journal.pone.0216224. PMC 6508868. PMID 31071129.
- ^ Aïmeur, Esma; Brassard, Gilles; Gambs, Sébastien (2006-06-07). "Machine Learning in a Quantum World". Advances in Artificial Intelligence. Lecture Notes in Computer Science. Vol. 4013. pp. 431–442. doi:10.1007/11766247_37. ISBN 978-3-540-34628-9.
- ^ Dunjko, Vedran; Taylor, Jacob M.; Briegel, Hans J. (2016-09-20). "Quantum-Enhanced Machine Learning". Physical Review Letters. 117 (13): 130501. arXiv:1610.08251. Bibcode:2016PhRvL.117m0501D. doi:10.1103/PhysRevLett.117.130501. PMID 27715099. S2CID 12698722.