User:Afk2231/Quantum machine learning

Quantum machine learning is the integration of quantum algorithms within machine learning programs.[1][2][3][4][5][6] 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.[7][8][9][10] 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.[11] This includes hybrid methods that involve both classical and quantum processing, where computationally difficult subroutines are outsourced to a quantum device.[12][13][14] These routines can be more complex in nature and executed faster on a quantum computer.[2] Furthermore, quantum algorithms can be used to analyze quantum states instead of classical data.[15][16] 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[17][18] or creating new quantum experiments.[19][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] Finally, researchers investigate more abstract notions of learning theory with respect to quantum information, sometimes referred to as "quantum learning theory".[26][27]

Four different approaches to combine the disciplines of quantum computing and machine learning.[28][29] The first letter refers to whether the system under study is classical or quantum, while the second letter defines whether a classical or quantum information processing device is used.

Machine learning with quantum computers

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Another approach to improving classical machine learning with quantum information processing uses amplitude amplification methods based on Grover's search algorithm, which has been shown to solve unstructured search problems with a quadratic speedup compared to classical algorithms. These quantum routines can be employed for learning algorithms that translate into an unstructured search task, as can be done, for instance, in the case of the k-medians[30] and the k-nearest neighbors algorithms.[7] Another application is a quadratic speedup in the training of perceptron.[31]

An example of amplitude amplification being used in a machine learning algorithm is Grover's search algorithm minimization. In which a subroutine uses Grover's search algorithm to find an element less than some less than some previously defined element. This can be done with an oracle that determines whether or not a state with a correponding element is less than the predefined one. Grover's algorithm can then find an element such that our condition is met. The minimization is initialized by some random element in our data set, and iteratively does this subroutine to find the minimum element in the data set. This minimization is notably used in quantum k-medians, and it has a speed up of at least   compared to classical versions of k-medians, where   is the number of data points and   is the number of clusters.[30]

Amplitude amplification is often combined with quantum walks to achieve the same quadratic speedup. Quantum walks have been proposed to enhance Google's PageRank algorithm[32] as well as the performance of reinforcement learning agents in the projective simulation framework.[33]

Quantum machine learning skepticism

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While Machine Learning itself is now not only a research field but an economically significant and fast growing industry and Quantum Computing is a well established field of both theoretical and experimental research, Quantum Machine Learning remains a purely theoretical field of studies. Attempts to prove experimentally concepts of Quantum Machine Learning remain rear and insufficient.

Many of the leading scientists that extensively publish in the field of Quantum Machine Learning beware about the extensive hype around the topic and are very restrained if ever speaking about any practical use of it in a foreseen future. Sophia Chen[34] collected some of the statements made by well known scientists in the field:

  • "I think we haven't done our homework yet. This is an extremely new scientific field," - physicist Maria Schuld of Canada-based quantum computing startup Xanadu.
  • "There is a lot more work that needs to be done before claiming quantum machine learning will actually work," - computer scientist Iordanis Kerenidis, the head of quantum algorithms at the Silicon Valley-based quantum computing startup QC Ware.
  • "I have not seen a single piece of evidence that there exists a meaningful [machine learning] task for which it would make sense to use a quantum computer and not a classical computer," - physicist Ryan Sweke of the Free University of Berlin in Germany.

“Don’t fall for the hype!” -  Frank Zickert[35], who is the author of probably the most practical book related to the subject beware that ”quantum computers are far away from advancing machine learning for their representation ability”, and even speaking about evaluation and optimization for any kind of useful task quantum supremacy is not yet achieved. Furthermore, nobody among the active researchers in the field make any forecasts about when it could possibly become practical.

  1. ^ Schuld, Maria; Petruccione, Francesco (2018). Supervised Learning with Quantum Computers. Quantum Science and Technology. doi:10.1007/978-3-319-96424-9. ISBN 978-3-319-96423-2.
  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.
  3. ^ Wittek, Peter (2014). Quantum Machine Learning: What Quantum Computing Means to Data Mining. Academic Press. ISBN 978-0-12-800953-6.
  4. ^ 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].
  5. ^ Biamonte, Jacob; Wittek, Peter; Pancotti, Nicola; 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.
  6. ^ Perdomo-Ortiz, Alejandro; Benedetti, Marcello; Realpe-Gómez, John; Biswas, Rupak (2018). "Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers". Quantum Science and Technology. 3 (3): 030502. arXiv:1708.09757. Bibcode:2018QS&T....3c0502P. doi:10.1088/2058-9565/aab859. S2CID 3963470.
  7. ^ a b 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. Bibcode:2014arXiv1401.2142W.
  8. ^ Lloyd, Seth; Mohseni, Masoud; Rebentrost, Patrick (2013). "Quantum algorithms for supervised and unsupervised machine learning". arXiv:1307.0411 [quant-ph].
  9. ^ 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.
  10. ^ Lee, Joong-Sung; Bang, Jeongho; Hong, Sunghyuk; Lee, Changhyoup; Seol, Kang Hee; Lee, Jinhyoung; Lee, Kwang-Geol (2019). "Experimental demonstration of quantum learning speedup with classical input data". Physical Review A. 99 (1): 012313. arXiv:1706.01561. Bibcode:2019PhRvA..99a2313L. doi:10.1103/PhysRevA.99.012313. S2CID 53977163.
  11. ^ 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.
  12. ^ 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.
  13. ^ Farhi, Edward; Neven, Hartmut (2018-02-16). "Classification with Quantum Neural Networks on Near Term Processors". arXiv:1802.06002 [quant-ph].
  14. ^ Schuld, Maria; Bocharov, Alex; Svore, Krysta; Wiebe, Nathan (2020). "Circuit-centric quantum classifiers". Physical Review A. 101 (3): 032308. arXiv:1804.00633. doi:10.1103/PhysRevA.101.032308. S2CID 49577148.
  15. ^ 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.
  16. ^ 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.
  17. ^ Broecker, Peter; Assaad, Fakher F.; Trebst, Simon (2017-07-03). "Quantum phase recognition via unsupervised machine learning". arXiv:1707.00663 [cond-mat.str-el].
  18. ^ 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.
  19. ^ 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.
  20. ^ 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.
  21. ^ 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.
  22. ^ Melnikov, Alexey A.; Nautrup, Hendrik Poulsen; Krenn, Mario; Dunjko, Vedran; Tiersch, Markus; Zeilinger, Anton; Briegel, Hans J. (1221). "Active learning machine learns to create new quantum experiments". Proceedings of the National Academy of Sciences. 115 (6): 1221–1226. arXiv:1706.00868. doi:10.1073/pnas.1714936115. ISSN 0027-8424. PMC 5819408. PMID 29348200.
  23. ^ 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.
  24. ^ 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.
  25. ^ Bény, Cédric (2013-01-14). "Deep learning and the renormalization group". arXiv:1301.3124 [quant-ph].
  26. ^ Arunachalam, Srinivasan; de Wolf, Ronald (2017-01-24). "A Survey of Quantum Learning Theory". arXiv:1701.06806 [quant-ph].
  27. ^ Sergioli, Giuseppe; Giuntini, Roberto; Freytes, Hector (2019-05-09). "A new Quantum approach to binary classification". PLOS ONE. 14 (5): e0216224. doi:10.1371/journal.pone.0216224. PMC 6508868. PMID 31071129.
  28. ^ Aïmeur, Esma; Brassard, Gilles; Gambs, Sébastien (2006-06-07). Machine Learning in a Quantum World. Lecture Notes in Computer Science. Vol. 4013. pp. 431–442. doi:10.1007/11766247_37. ISBN 978-3-540-34628-9. {{cite book}}: |journal= ignored (help)
  29. ^ 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.
  30. ^ a b Aïmeur, Esma; Brassard, Gilles; Gambs, Sébastien (2013-02-01). "Quantum speed-up for unsupervised learning". Machine Learning. 90 (2): 261–287. doi:10.1007/s10994-012-5316-5. ISSN 0885-6125.
  31. ^ Wiebe, Nathan; Kapoor, Ashish; Svore, Krysta M. (2016). Quantum Perceptron Models. Advances in Neural Information Processing Systems. Vol. 29. pp. 3999–4007. arXiv:1602.04799. Bibcode:2016arXiv160204799W. {{cite conference}}: Cite has empty unknown parameters: |1= and |2= (help)
  32. ^ Paparo, Giuseppe Davide; Martin-Delgado, Miguel Angel (2012). "Google in a Quantum Network". Scientific Reports. 2 (444): 444. arXiv:1112.2079. Bibcode:2012NatSR...2E.444P. doi:10.1038/srep00444. PMC 3370332. PMID 22685626.
  33. ^ 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. arXiv:1401.4997. Bibcode:2014PhRvX...4c1002P. doi:10.1103/PhysRevX.4.031002. S2CID 54652978.
  34. ^ "Can quantum machine learning move beyond its own hype?". Protocol. 2020-05-04. Retrieved 2020-10-27.
  35. ^ Zickert, Frank (2020-09-23). "Quantum Machine Learning". Medium. Retrieved 2020-10-27.