Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

Quantum Machine Learning for Computational Methods in Engineering: A Systematic Review

  • Review article
  • Published:
Archives of Computational Methods in Engineering Aims and scope Submit manuscript

Abstract

Quantum Machine Learning (QML) has emerged as a unique computing area. The utilization of quantum technology in machine learning can solve complex problems (unsolvable using classical computational methodologies). The revolutionary paradigms potential has spurred scientific research and progress. Therefore, a highly essential exploration is needed to extract scientific breakthrough paths. The proposed work supports the concept by providing a scientometric analysis of QML scientific literature for the period 2003–2023, gathered from the Web of Science database. The study explores the powerful machine learning techniques in the quantum realm. The scientometric implication of the article provides deep insights into the publication and citation pattern, geographical distribution analysis, document co-citation, and keyword co-occurrence network analysis. The research findings highlight the predominant use of algorithms such as quantum support vector machines, quantum neural networks, and Q-learning. Notably active research hotspots in this field include drug design and discovery, quantum control, optimization, error-correction, and quantum state tomography. Additionally, collaborative efforts are evident in the domains of quantum unsupervised and reinforcement machine learning. The overall inference of QML literature portrays insightful recommendations and research directions for the academic community.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Bhatia M, Sood SK (2020) Quantum computing-inspired network optimization for IoT applications. IEEE Internet Things J 7(6):5590–5598

    Google Scholar 

  2. Cerezo M, Verdon G, Huang HY, Cincio L, Coles PJ (2022) Challenges and opportunities in quantum machine learning. Nat Comput Sci 2(9):567–576

    Google Scholar 

  3. Schuld M, Sinayskiy I, Petruccione F (2015) An introduction to quantum machine learning. Contemp Phys 56(2):172–185

    Google Scholar 

  4. Pande M, Mulay P (2020) Bibliometric survey of quantum machine learning. Sci Technol Libr 39(4):369–382

    Google Scholar 

  5. Jeswal S, Chakraverty S (2019) Recent developments and applications in quantum neural network: a review. Arch Comput Methods Eng 26:793–807

    MathSciNet  Google Scholar 

  6. Wang Z, Xu M, Zhang Y (2022) Review of quantum image processing. Arch Comput Methods Eng 29(2):737–761

    MathSciNet  Google Scholar 

  7. Dhawan S, Gupta B, Mamdapur GMN (2021) Quantum machine learning: a scientometric assessment of global publications during 1999–2020. Int J Knowl Content Dev Technol 11(3):29

    Google Scholar 

  8. Sood V, Chauhan RP (2023) Archives of quantum computing: research progress and challenges. Arch Comput Methods Eng. https://doi.org/10.1007/s11831-023-09973-2

    Article  Google Scholar 

  9. Jadhav A, Rasool A, Gyanchandani M (2023) Quantum machine learning: scope for real-world problems. Procedia Comput Sci 218:2612–2625 (International conference on machine learning and data engineering)

    Google Scholar 

  10. Zhang Y, Ni Q (2020) Recent advances in quantum machine learning. Quantum Eng 2(1):e34

    Google Scholar 

  11. Melnikov A, Kordzanganeh M, Alodjants A, Lee RK (2023) Quantum machine learning: from physics to software engineering. Adv Phys X 8(1):2165452. https://doi.org/10.1080/23746149.2023.2165452

    Article  Google Scholar 

  12. Khan TM, Robles-Kelly A (2020) Machine learning: quantum vs classical. IEEE Access 8:219275–219294

    Google Scholar 

  13. Houssein EH, Abohashima Z, Elhoseny M, Mohamed WM (2022) Machine learning in the quantum realm: the state-of-the-art, challenges, and future vision. Expert Syst Appl 194:116512. https://doi.org/10.1016/j.eswa.2022.116512

    Article  Google Scholar 

  14. Verma P, Sood SK, Kaur H (2020) A fog-cloud based cyber physical system for ulcerative colitis diagnosis and stage classification and management. Microprocess Microsyst 72:102929

    Google Scholar 

  15. Wei L, Liu H, Xu J, Shi L, Shan Z, Zhao B, Gao Y (2023) Quantum machine learning in medical image analysis: a survey. Neurocomputing 525:42–53. https://doi.org/10.1016/j.neucom.2023.01.049

    Article  Google Scholar 

  16. Bhatia M, Sood SK, Kaur S (2019) Quantum-based predictive fog scheduler for IoT applications. Comput Ind 111:51–67

    Google Scholar 

  17. Šćekić M, Šćepanović S, Mitrović S (2022) Implementation of quantum machine learning algorithms: a literature review. In: 2022 11th Mediterranean conference on embedded computing (MECO). IEEE, pp 1–4

  18. Zeguendry A, Jarir Z, Quafafou M (2023) Quantum machine learning: a review and case studies. Entropy 25(2):287

    MathSciNet  Google Scholar 

  19. Sood SK, Pooja (2023) Quantum computing review: a decade of research. IEEE Trans Eng Manag. https://doi.org/10.1109/TEM.2023.3284689

  20. Singh J, Bhangu KS (2023) Contemporary quantum computing use cases: taxonomy, review and challenges. Arch Comput Methods Eng 30(1):615–638

    Google Scholar 

  21. Bhatia M, Sood S, Sood V (2020) A novel quantum-inspired solution for high-performance energy-efficient data acquisition from IoT networks. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-020-02494-x

    Article  Google Scholar 

  22. Biamonte J, Wittek P, Pancotti N, Rebentrost P, Wiebe N, Lloyd S (2017) Quantum machine learning. Nature 549(7671):195–202

    Google Scholar 

  23. García DP, Cruz-Benito J, García-Peñalvo FJ (2022) Systematic literature review: quantum machine learning and its applications. arXiv Preprint. arXiv:2201.04093

  24. Nawaz SJ, Sharma SK, Wyne S, Patwary MN, Asaduzzaman M (2019) Quantum machine learning for 6G communication networks: state-of-the-art and vision for the future. IEEE Access 7:46317–46350

    Google Scholar 

  25. Batra K, Zorn KM, Foil DH, Minerali E, Gawriljuk VO, Lane TR, Ekins S (2021) Quantum machine learning algorithms for drug discovery applications. J Chem Inf Model 61(6):2641–2647

    Google Scholar 

  26. Liu N, Rebentrost P (2018) Quantum machine learning for quantum anomaly detection. Phys Rev A 97(4):042315

    Google Scholar 

  27. Sood SK, Rawat KS, Sharma G (2022) 3-D printing technologies from infancy to recent times: a scientometric review. IEEE Trans Eng Manag. https://doi.org/10.1109/TEM.2021.3134128

    Article  Google Scholar 

  28. Sood V, Chauhan RP (2023) Towards quantum state preparation with materials science: an analytical review. Int J Quantum Chem 123(18):e27148

    Google Scholar 

  29. Sood SK, Rawat KS, Kumar D (2023) Emerging trends of ICT in airborne disease prevention. ACM Trans Internet Technol 22(4):1–18

    Google Scholar 

  30. Neelam S, Sood SK (2020) A scientometric review of global research on smart disaster management. IEEE Trans Eng Manag 68(1):317–329

    Google Scholar 

  31. Sood SK, Rawat KS, Kumar D (2022) A visual review of artificial intelligence and industry 4.0 in healthcare. Comput Electr Eng 101:107948

    Google Scholar 

  32. Sood SK, Rawat KS, Kumar D (2022) Analytical mapping of information and communication technology in emerging infectious diseases using CiteSpace. Telemat Inform p 101796. https://www.sciencedirect.com/science/article/pii/S0736585322000296

  33. Rawat KS, Sood SK (2021) Emerging trends and global scope of big data analytics: a scientometric analysis. Qual Quant 55:1371–1396

    Google Scholar 

  34. Rupp M, Tkatchenko A, Müller KR, von Lilienfeld OA (2012) Fast and accurate modeling of molecular atomization energies with machine learning. Phys Rev Lett 108:058301. https://doi.org/10.1103/PhysRevLett.108.058301

    Article  Google Scholar 

  35. Katritzky AR, Lobanov VS, Karelson M (1995) QSPR: the correlation and quantitative prediction of chemical and physical properties from structure. Chem Soc Rev 24:279–287. https://doi.org/10.1039/CS9952400279

    Article  Google Scholar 

  36. Staikova M, Messih P, Lei YD, Wania F, Donaldson DJ (2005) Prediction of subcooled vapor pressures of nonpolar organic compounds using a one-parameter QSPR. J Chem Eng Data 50(2):438–443. https://doi.org/10.1021/je049732n

    Article  Google Scholar 

  37. Al-Rabadi AN (2012) Soft computation using artificial neural estimation and linear matrix inequality transmutation for controlling singularly-perturbed closed time-independent quantum computation systems, part A: basics and approach. Intell Autom Soft Comput 18(1):75–95. https://doi.org/10.1080/10798587.2012.10643228

    Article  Google Scholar 

  38. Gao J, Wang X, Li X, Yu X, Wang H (2006) Prediction of polyamide properties using quantum-chemical methods and BP artificial neural networks. J Mol Model 12:513–520

    Google Scholar 

  39. Yoshioka N, Akagi Y, Katsura H (2018) Learning disordered topological phases by statistical recovery of symmetry. Phys Rev B 97:205110. https://doi.org/10.1103/PhysRevB.97.205110

    Article  Google Scholar 

  40. Liang NY, Huang GB, Saratchandran P, Sundararajan N (2006) A fast and accurate online sequential learning algorithm for feedforward networks. IEEE Trans Neural Netw 17(6):1411–1423

    Google Scholar 

  41. Käming N, Dawid A, Kottmann K, Lewenstein M, Sengstock K, Dauphin A, Weitenberg C (2021) Unsupervised machine learning of topological phase transitions from experimental data. Mach Learn Sci Technol 2(3):035037. https://doi.org/10.1088/2632-2153/abffe7

    Article  Google Scholar 

  42. Guo M, Liu H, Li Y, Li W, Gao F, Qin S, Wen Q (2022) Quantum algorithms for anomaly detection using amplitude estimation. Physica A 604:127936. https://doi.org/10.1016/j.physa.2022.127936

    Article  MathSciNet  Google Scholar 

  43. Flamini F, Spagnolo N, Sciarrino F (2019) Visual assessment of multi-photon interference. Quantum Sci Technol 4(2):024008. https://doi.org/10.1088/2058-9565/ab04fc

    Article  Google Scholar 

  44. Mahmoud HAH (2023) Transfer learning in inorganic compounds’ crystal structure classification. Curr Comput Aided Drug Des 13(1):87

    Google Scholar 

  45. Carleo G, Troyer M (2017) Solving the quantum many-body problem with artificial neural networks. Science 355(6325):602–606

    MathSciNet  Google Scholar 

  46. Tilly J, Chen H, Cao S, Picozzi D, Setia K, Li Y, Grant E, Wossnig L, Rungger I, Booth GH et al (2022) The variational quantum eigensolver: a review of methods and best practices. Phys Rep 986:1–128

    MathSciNet  Google Scholar 

  47. Handley CM, Popelier PL (2009) Dynamically polarizable water potential based on multipole moments trained by machine learning. J Chem Theory Comput 5(6):1474–1489

    Google Scholar 

  48. Funes-Ardoiz I, Schoenebeck F (2020) Established and emerging computational tools to study homogeneous catalysis—from quantum mechanics to machine learning. Chem 6(8):1904–1913. https://doi.org/10.1016/j.chempr.2020.07.008

    Article  Google Scholar 

  49. Buttingsrud B, King RD, Alsberg BK (2007) An alignment-free methodology for modelling field-based 3D-structure activity relationships using inductive logic programming. J Chemom A 21(12):509–519

    Google Scholar 

  50. Giralt F, Espinosa G, Arenas A, Ferre-Gine J, Amat L, Girones X, Carbó-Dorca R, Cohen Y (2004) Estimation of infinite dilution activity coefficients of organic compounds in water with neural classifiers. AIChE J 50(6):1315–1343

    Google Scholar 

  51. Fösel T, Tighineanu P, Weiss T, Marquardt F (2018) Reinforcement learning with neural networks for quantum feedback. Phys Rev X 8:031084. https://doi.org/10.1103/PhysRevX.8.031084

    Article  Google Scholar 

  52. Chen SYC, Yang CHH, Qi J, Chen PY, Ma X, Goan HS (2020) Variational quantum circuits for deep reinforcement learning. IEEE Access 8:141007–141024. https://doi.org/10.1109/ACCESS.2020.3010470

    Article  Google Scholar 

  53. Dong D, Chen C, Zhang C, Chen Z (2006) Quantum robot: structure, algorithms and applications. Robotica 24(4):513–521. https://doi.org/10.1017/S0263574705002596

    Article  Google Scholar 

  54. von Lilienfeld OA, Müller KR, Tkatchenko A (2020) Exploring chemical compound space with quantum-based machine learning. Nat Rev Chem 4(7):347–358

    Google Scholar 

  55. Sanches F, Weinberg S, Ide T, Kamiya K (2022) Short quantum circuits in reinforcement learning policies for the vehicle routing problem. Phys Rev A 105(6):062403

    MathSciNet  Google Scholar 

  56. Dong D, Chen C, Tarn TJ, Pechen A, Rabitz H (2008) Incoherent control of quantum systems with wavefunction-controllable subspaces via quantum reinforcement learning. IEEE Trans Syst Man Cybern B (Cybern) 38(4):957–962

    Google Scholar 

  57. Cooper CH (2021) Exploring potential applications of quantum computing in transportation modelling. IEEE Trans Intell Transp Syst 23(9):14712–14720

    Google Scholar 

  58. Schütt KT, Arbabzadah F, Chmiela S, Müller KR, Tkatchenko A (2017) Quantum-chemical insights from deep tensor neural networks. Nat Commun 8(1):13890

    Google Scholar 

  59. McClean JR, Romero J, Babbush R, Aspuru-Guzik A (2016) The theory of variational hybrid quantum-classical algorithms. New J Phys 18(2):023023. https://doi.org/10.1088/1367-2630/18/2/023023

    Article  Google Scholar 

  60. Havlíček V, Córcoles AD, Temme K, Harrow AW, Kandala A, Chow JM, Gambetta JM (2019) Supervised learning with quantum-enhanced feature spaces. Nature 567(7747):209–212

    Google Scholar 

  61. Cong I, Choi S, Lukin MD (2019) Quantum convolutional neural networks. Nat Phys 15(12):1273–1278

    Google Scholar 

  62. Schuld M, Bocharov A, Svore KM, Wiebe N (2020) Circuit-centric quantum classifiers. Phys Rev A 101:032308. https://doi.org/10.1103/PhysRevA.101.032308

    Article  MathSciNet  Google Scholar 

  63. Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504–507

    MathSciNet  Google Scholar 

  64. Torlai G, Melko RG (2016) Learning thermodynamics with Boltzmann machines. Phys Rev B 94(16):165134

    Google Scholar 

  65. Benedetti M, Realpe-Gómez J, Biswas R, Perdomo-Ortiz A (2016) Estimation of effective temperatures in quantum annealers for sampling applications: a case study with possible applications in deep learning. Phys Rev A 94(2):022308

    Google Scholar 

  66. Torlai G, Mazzola G, Carrasquilla J, Troyer M, Melko R, Carleo G (2018) Neural-network quantum state tomography. Nat Phys 14(5):447–450

    Google Scholar 

  67. Preskill J (2018) Quantum computing in the NISQ era and beyond. Quantum 2:79

    Google Scholar 

  68. Paparo GD, Dunjko V, Makmal A, Martin-Delgado MA, Briegel HJ (2014) Quantum speedup for active learning agents. Phys Rev X 4(3):031002

    Google Scholar 

  69. Smith JS, Isayev O, Roitberg AE (2017) ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost. Chem Sci 8(4):3192–3203

    Google Scholar 

  70. Deng DL, Li X, Sarma SD (2017) Quantum entanglement in neural network states. Phys Rev X 7(2):021021

    Google Scholar 

  71. Zhang XM, Wei Z, Asad R, Yang XC, Wang X (2019) When does reinforcement learning stand out in quantum control? A comparative study on state preparation. npj Quantum Inf 5(1):85

    Google Scholar 

  72. LaRose R, Coyle B (2020) Robust data encodings for quantum classifiers. Phys Rev A 102:032420. https://doi.org/10.1103/PhysRevA.102.032420

    Article  Google Scholar 

  73. Ayanzadeh R, Halem M, Finin T (2020) Reinforcement quantum annealing: a hybrid quantum learning automata. Sci Rep 10(1):1–11

    Google Scholar 

Download references

Author information

Author notes

  1. Sandeep Kumar Sood and Monika Agrewal have contributed equally to this work.

Authors and Affiliations

  1. Department of Computer Applications, National Institute of Technology Kurukshetra, Kurukshetra, Haryana, 136119, India

    Sandeep Kumar Sood & Monika Agrewal

Authors
  1. Sandeep Kumar Sood
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Monika Agrewal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Monika Agrewal.

Ethics declarations

Conflict of interest

The authors have no conflicts of interest to declare.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sood, S.K., Agrewal, M. Quantum Machine Learning for Computational Methods in Engineering: A Systematic Review. Arch Computat Methods Eng 31, 1555–1577 (2024). https://doi.org/10.1007/s11831-023-10027-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11831-023-10027-w

Search

Navigation