Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon openaccess Forthcoming applications of quantum computing: peeking into the future

We all have been using classical computers for a long time. Quantum computing uses the phenomena of quantum mechanics like superposition and entanglement. Quantum computations can help achieve for the breakthroughs we have been looking for in science, machine learning, financial planning, medicine, etc., where classical computers’ computing power is not enough. It was not long back when quantum computing's applications in our life were all just theoretical. However, to utilise the power of quantum computations for real-life applications, several recent developments have been made. Keeping that in mind, this study aims to explore the existing and upcoming applications of quantum computing. In this study, they start with an introduction of quantum computing fundamentals, following which, they give a brief overview of various applications of quantum computing in several significant areas of computer science, such as cryptography, machine learning, deep learning, and quantum simulations. They also cover various real-life scenarios such as risk analysis, logistics, and satellite communication.

References

    1. 1)
      • 6. Freeman, J.: ‘The progression of lattice-based cryptography’, 2018.
    2. 2)
      • 39. Biamonte, J., Wittek, P., Pancotti, N., et al: ‘Quantum machine learning’, Nature, 2017, 549, (7671), pp. 195202.
    3. 3)
      • 28. Lloyd, S., Mohseni, M., Rebentrost, P.: ‘Quantum algorithms for supervised and unsupervised machine learning’, arXiv preprint arXiv:13070411, 2013.
    4. 4)
      • 22. Wiebe, N., Braun, D., Lloyd, S.: ‘Quantum algorithm for data fitting’, Phys. Rev. Lett., 2012, 109, (5), p. 050505.
    5. 5)
      • 9. Wang, L., Kowk, S., Ip, W.: ‘Design of an improved quantum-inspired evolutionary algorithm for a transportation problem in logistics systems’, J. Intell. Manuf., 2012, 23, (6), pp. 22272236.
    6. 6)
      • 45. Feynman, R.P.: ‘Simulating physics with computers’, Int. J. Theor. Phys., 1982, 21, pp. 467488, https://doi.org/10.1007/BF02650179.
    7. 7)
      • 52. Bacsardi, L.: ‘On the way to quantum-based satellite communication’, IEEE Commun. Mag., 2013, 51, (8), pp. 5055.
    8. 8)
      • 10. Proos, J., Zalka, C.: ‘Shor's discrete logarithm quantum algorithm for elliptic curves’, arXiv preprint quant-ph/0301141, 2003.
    9. 9)
      • 30. Wiebe, N., Kapoor, A., Svore, K.: ‘Quantum algorithms for nearest-neighbor methods for supervised and unsupervised learning’, arXiv preprint arXiv:14012142, 2014.
    10. 10)
      • 48. Woerner, S., Egger, D.J.: ‘Quantum risk analysis’, Npj Quantum Inf., 2019, 5, (1), pp. 18.
    11. 11)
      • 23. Alba Cervera-Lierta: ‘Quantum annealing’, Available at https://medium.com/@quantum_wa/quantum-annealing-cdb129e96601, accessed 28 October 2020.
    12. 12)
      • 49. Stefanov, A., Gisin, N., Guinnard, O., et al: ‘Optical quantum random number generator’, J. Mod. Opt., 2000, 47, (4), pp. 595598.
    13. 13)
      • 16. Durr, C., Hoyer, P.: ‘A quantum algorithm for finding the minimum’, arXiv preprint quant-ph/9607014, 1996.
    14. 14)
      • 32. Rebentrost, P., Mohseni, M., Lloyd, S.: ‘Quantum support vector machine for big data classification’, Phys. Rev. Lett., 2014, 113, (13), p. 130503.
    15. 15)
      • 12. Kuperberg, G.: ‘A subexponential-time quantum algorithm for the dihedral hidden subgroup problem’, SIAM J. Comput., 2005, 35, (1), pp. 170188.
    16. 16)
      • 25. Burges, C.J.: ‘Factoring as optimization’, Microsoft Research MSR-TR-200, 2002.
    17. 17)
      • 47. Kroese, D.P., Brereton, T., Taimre, T., et al: ‘Why the Monte Carlo method is so important today’, Wiley Interdiscip. Rev.: Comput. Stat., 2014, 6, (6), pp. 386392.
    18. 18)
      • 26. Xu, N., Zhu, J., Lu, D., et al: ‘Quantum factorization of 143 on a dipolar-coupling nuclear magnetic resonance system’, Phys. Rev. Lett., 2012, 108, (13), p. 130501.
    19. 19)
      • 14. Sysoev, S.: ‘The effective solving of the tasks from np by a quantum computer’, arXiv preprint arXiv:14016030, 2014.
    20. 20)
      • 36. Behrman, E.C., Steck, J.E.: ‘A quantum neural network computes its own relative phase’. 2013 IEEE Symp. on Swarm Intelligence (SIS), Singapore, 2013, pp. 119124.
    21. 21)
      • 27. Aïmeur, E., Brassard, G., Gambs, S.: ‘Machine learning in a quantum world’. 19th Conference of the Canadian Society for Computational Studies of Intelligence, 2006, Quebec City, Quebec, Canada pp. 431442.
    22. 22)
      • 17. Dürr, C., Heiligman, M., HOyer, P., et al: ‘Quantum query complexity of some graph problems’, SIAM J. Comput., 2006, 35, (6), pp. 13101328.
    23. 23)
      • 33. Chatterjee, R., Yu, T.: ‘Generalized coherent states, reproducing kernels, and quantum support vector machines’, arXiv preprint arXiv:161203713, 2016.
    24. 24)
      • 34. Zhao, Z., Fitzsimons, J.K., Fitzsimons, J.F.: ‘Quantum-assisted Gaussian process regression’, Phys. Rev. A, 2019, 99, (5), p. 052331.
    25. 25)
      • 29. Buhrman, H., Cleve, R., Watrous, J., et al: ‘Quantum fingerprinting’, Phys. Rev. Lett., 2001, 87, (16), p. 167902.
    26. 26)
      • 37. Gupta, S., Zia, R.: ‘Quantum neural networks’, J. Comput. Syst. Sci., 2001, 63, (3), pp. 355383.
    27. 27)
      • 43. Srinivasan, S., Gordon, G., Boots, B.: ‘Learning hidden quantum Markov models’, arXiv preprint arXiv:171009016, 2017.
    28. 28)
      • 38. Adachi, S.H., Henderson, M.P.: ‘Application of quantum annealing to training of deep neural networks’, arXiv preprint arXiv:151006356, 2015.
    29. 29)
      • 7. Shor, P.W.: ‘Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer’, SIAM Rev., 1999, 41, (2), pp. 303332.
    30. 30)
      • 13. Figgatt, C., Maslov, D., Landsman, K., et al: ‘Complete 3-qubit Grover search on a programmable quantum computer’, Nat. Commun., 2017, 8, (1), pp. 19.
    31. 31)
      • 19. Farhi, E., Goldstone, J., Gutmann, S., et al: ‘Quantum computation by adiabatic evolution’, arXiv preprint quant-ph/0001106, 2000.
    32. 32)
      • 35. Rigatos, G.G., Tzafestas, S.G.: ‘Neurodynamics and attractors in quantum associative memories’, Integr. Comput.-Aided Eng., 2007, 14, (3), pp. 225242.
    33. 33)
      • 46. Yang, C.S., Lu, C.S., Xu, J., et al: ‘Evaluating green supply chain management capability, environmental performance, and competitiveness in container shipping context’, J. Eastern Asia Soc. Transp. Stud., 2013, 10, pp. 22742293.
    34. 34)
      • 31. Anguita, D., Ridella, S., Rivieccio, F., et al: ‘Quantum optimization for training support vector machines’, Neural Netw., 2003, 16, (5-6), pp. 763770.
    35. 35)
      • 5. Bernstein, D.J., Lange, T.: ‘Post-quantum cryptography-dealing with the fallout of physics success’, IACR Cryptol ePrint Arch, 2017, 2017, p. 314.
    36. 36)
      • 3. Institute of Quantum Computing, University of Waterloo: ‘Quantum computing 101’, Available at https://uwaterloo.ca/institute-for-quantum-computing/quantum-computing-101, accessed 12 September 2020.
    37. 37)
      • 42. Clark, L.A., Huang, W., Barlow, T.M., et al: ‘Hidden quantum Markov models and open quantum systems with instantaneous feedback’, Emerg., Complex. Comput., 2015, 14, Springer, Cham, pp. 143151. Available at http://dx.doi.org/10.1007/978-3-319-10759-2_16.
    38. 38)
      • 1. IBM: ‘A new kind of computing’, Available at https://www.ibm.com/quantum-computing/learn/what-is-quantum-computing/, accessed 12 September 2020.
    39. 39)
      • 15. Brassard, G., Hoyer, P., Mosca, M., et al: ‘Quantum amplitude amplification and estimation’, Contemp. Math., 2002, 305, pp. 5374.
    40. 40)
      • 24. Denchev, V.S., Boixo, S., Isakov, S.V., et al: ‘What is the computational value of finite-range tunneling?’, Phys. Rev. X, 2016, 6, (3), p. 031015.
    41. 41)
      • 2. Science Alert: ‘How Do quantum computers work?’, Available at https://www.sciencealert.com/quantum-computers, accessed 12 September 2020.
    42. 42)
      • 41. Monras, A., Beige, A., Wiesner, K.: ‘Hidden quantum Markov models and nonadaptive read-out of many-body states’, arXiv preprint arXiv:10022337, 2010.
    43. 43)
      • 11. Hallgren, S.: ‘Polynomial-time quantum algorithms for Pell's equation and the principal ideal problem’, J. ACM (JACM), 2007, 54, (1), pp. 119.
    44. 44)
      • 4. Harrow, A.W., Hassidim, A., Lloyd, S.: ‘Quantum algorithm for linear systems of equations’, Phys. Rev. Lett., 2009, 103, (15), p. 150502.
    45. 45)
      • 50. Jennewein, T., Achleitner, U., Weihs, G., et al: ‘A fast and compact quantum random number generator’, Rev. Sci. Instrum., 2000, 71, (4), pp. 16751680.
    46. 46)
      • 51. Lalanne, P., Rodier, J.C., Chavel, P.H., et al: ‘Optoelectronic devices for Boltzmann machines and simulated annealing’, Opt. Eng., 1993, 32, (8), pp. 19041915.
    47. 47)
      • 18. Ramesh, H., Vinay, V.: ‘String matching in o(n + m) quantum time’, J. Discret. Algorithms, 2003, 1, (1), pp. 103110.
    48. 48)
      • 44. Buluta, I., Nori, F.: ‘Quantum simulators’, Science, 2009, 326, (5949), pp. 108111.
    49. 49)
      • 40. Schuld, M., Sinayskiy, I., Petruccione, F.: ‘The quest for a quantum neural network’, Quantum Inf. Process., 2014, 13, (11), pp. 25672586.
    50. 50)
      • 53. Bacsardi, L.: ‘Satellite communication over quantum channel’, Acta Astronaut., 2007, 61, (1–6), pp. 151159.
    51. 51)
      • 20. Clader, B.D., Jacobs, B.C., Sprouse, C.R.: ‘Preconditioned quantum linear system algorithm’, Phys. Rev. Lett., 2013, 110, (25), p. 250504.
    52. 52)
      • 21. Berry, D.W.: ‘High-order quantum algorithm for solving linear differential equations’, J. Phys. A: Math. Theor., 2014, 47, (10), p. 105301.
    53. 53)
      • 8. Grover, L.K.: ‘Quantum mechanics helps in searching for a needle in a haystack’, Phys. Rev. Lett., 1997, 79, (2), p. 325.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-qtc.2020.0026
Loading

Related content

content/journals/10.1049/iet-qtc.2020.0026
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address