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Quantum Data Management and Quantum Machine Learning for Data Management: State-of-the-Art and Open Challenges

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Intelligent Systems and Machine Learning (ICISML 2022)

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Abstract

Quantum computing is an emerging technology and has yet to be exploited by industries to implement practical applications. Research has already laid the foundation for figuring out the benefits of quantum computing for these applications. In this paper, we provide a short overview of the state-of-the-art in data management issues that can be solved by quantum computers and especially by quantum machine learning approaches. Furthermore, we discuss what data management can do to support quantum computing and quantum machine learning.

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Notes

  1. 1.

    We regard here quantum algorithms and quantum-inspired [50] algorithms as quantum counterparts, although quantum-inspired algorithm are designed to run on classical hardware, but are inspired from the quantum computing concept.

References

  1. Abohashima, Z., Elhosen, M., Houssein, E.H., Mohamed, W.M.: Classification with quantum machine learning: a survey. arXiv arXiv:2006.12270 (2020). https://doi.org/10.48550/ARXIV.2006.12270

  2. Agrawal, R., Kaur, B., Sharma, S.: Quantum based whale optimization algorithm for wrapper feature selection. Appl. Soft Comput. 89, 106092 (2020). https://doi.org/10.1016/j.asoc.2020.106092

  3. Aharonov, Y., Davidovich, L., Zagury, N.: Quantum random walks. Phys. Rev. A 48(2), 1687–1690 (1993). https://doi.org/10.1103/physreva.48.1687

  4. Akdere, M., Çetintemel, U., Riondato, M., Upfal, E., Zdonik, S.B.: Learning-based query performance modeling and prediction. In: 2012 IEEE 28th International Conference on Data Engineering, pp. 390–401. IEEE (2012)

    Google Scholar 

  5. Ambainis, A.: Variable time amplitude amplification and quantum algorithms for linear algebra problems (2012). https://doi.org/10.4230/LIPICS.STACS.2012.636

  6. Ambainis, A., Bach, E., Nayak, A., Vishwanath, A., Watrous, J.: One-dimensional quantum walks. In: Proceedings of the Thirty-Third Annual ACM Symposium on Theory of Computing - STOC 2001. ACM Press (2001). https://doi.org/10.1145/380752.380757

  7. Ambainis, A., Balodis, K., Iraids, J., Kokainis, M., Prūsis, K., Vihrovs, J.: Quantum speedups for exponential-time dynamic programming algorithms. In: Proceedings of the Thirtieth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 1783–1793. Society for Industrial and Applied Mathematics, January 2019. https://doi.org/10.1137/1.9781611975482.107

  8. An, D., Lin, L.: Quantum linear system solver based on time-optimal adiabatic quantum computing and quantum approximate optimization algorithm. ACM Trans. Quantum Comput. 3(2), 1–28 (2022). https://doi.org/10.1145/3498331

  9. Barto, A.G., Sutton, R.S., Brouwer, P.S.: Associative search network: a reinforcement learning associative memory. Biol. Cybern. 40(3), 201–211 (1981). https://doi.org/10.1007/bf00453370

  10. Bittner, T., Groppe, S.: Avoiding blocking by scheduling transactions using quantum annealing. In: Proceedings of the 24th Symposium on International Database Engineering & Applications. ACM, August 2020. https://doi.org/10.1145/3410566.3410593

  11. Bittner, T., Groppe, S.: Hardware accelerating the optimization of transaction schedules via quantum annealing by avoiding blocking. Open J. Cloud Comput. (OJCC) 7(1), 1–21 (2020). http://nbn-resolving.de/urn:nbn:de:101:1-2020112218332015343957

  12. Caro, M.C., et al.: Generalization in quantum machine learning from few training data. Nat. Commun. 13(1) (2022)

    Google Scholar 

  13. Chen, J., et al.: Efficient join order selection learning with graph-based representation. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2022, pp. 97–107. Association for Computing Machinery, New York (2022). https://doi.org/10.1145/3534678.3539303

  14. Childs, A.M., Kothari, R., Somma, R.D.: Quantum algorithm for systems of linear equations with exponentially improved dependence on precision. SIAM J. Comput. 46(6), 1920–1950 (2017). https://doi.org/10.1137/16m1087072

  15. Ciliberto, C., et al.: Quantum machine learning: a classical perspective. Proc. Roy. Soc. A Math. Phys. Eng. Sci. 474(2209), 20170551 (2018). https://doi.org/10.1098/rspa.2017.0551

    Article  MathSciNet  MATH  Google Scholar 

  16. Colorni, A., Dorigo, M., Maniezzo, V., Varela, F.J., Bourgine, P.E.: Distributed optimization by ant colonies. In: Proceedings of the First European Conference on Artificial Life, pp. 134–142 (1991)

    Google Scholar 

  17. Dantzig, G.B.: Linear programming. Oper. Res. 50(1), 42–47 (2002). https://doi.org/10.1287/opre.50.1.42.17798

  18. Dawid, A., et al.: Modern applications of machine learning in quantum sciences. arXiv preprint arXiv:2204.04198 (2022)

  19. Doherty, M.: Quantum accelerators: a new trajectory of quantum computers. Digitale Welt 5(2), 74–79 (2021). https://doi.org/10.1007/s42354-021-0342-8

    Article  Google Scholar 

  20. Dong, D., Chen, C., Chen, Z.: Quantum reinforcement learning. In: Wang, L., Chen, K., Ong, Y.S. (eds.) ICNC 2005. LNCS, vol. 3611, pp. 686–689. Springer, Heidelberg (2005). https://doi.org/10.1007/11539117_97

    Chapter  Google Scholar 

  21. Dorigo, M., Birattari, M., Stutzle, T.: Ant colony optimization. IEEE Comput. Intell. Mag. 1(4), 28–39 (2006)

    Article  Google Scholar 

  22. Dürr, C., Høyer, P.: A quantum algorithm for finding the minimum (1996). https://doi.org/10.48550/ARXIV.QUANT-PH/9607014. https://arxiv.org/abs/quant-ph/9607014

  23. Eddy, S.R.: What is dynamic programming? Nat. Biotechnol. 22(7), 909–910 (2004). https://doi.org/10.1038/nbt0704-909

    Article  Google Scholar 

  24. Farhi, E., Goldstone, J., Gutmann, S.: A quantum approximate optimization algorithm, November 2014. http://arxiv.org/abs/1411.4028

  25. Galindo-Legaria, C.A., Pellenkoft, A., Kersten, M.L.: Fast, randomized join-order selection - why use transformations? In: Proceedings of the 20th International Conference on Very Large Data Bases, VLDB 1994, pp. 85–95. Morgan Kaufmann Publishers Inc., San Francisco (1994)

    Google Scholar 

  26. Ganapathi, A., et al.: Predicting multiple metrics for queries: better decisions enabled by machine learning. In: 2009 IEEE 25th International Conference on Data Engineering, pp. 592–603. IEEE (2009)

    Google Scholar 

  27. Groppe, S.: Semantic hybrid multi-model multi-platform (SHM3P) databases. In: International Semantic Intelligence Conference (ISIC 2021), New Delhi (hybrid), India, pp. 16–26. CEUR (2021). http://ceur-ws.org/Vol-2786/Paper2.pdf

  28. Groppe, S., Groppe, J.: Hybrid multi-model multi-platform (HM3P) databases. In: Proceedings of the 9th International Conference on Data Science, Technology and Applications (DATA) (2020). https://doi.org/10.5220/0009802401770184

  29. Groppe, S., Groppe, J.: Optimizing transaction schedules on universal quantum computers via code generation for Grover’s search algorithm. In: 25th International Database Engineering & Applications Symposium. ACM, July 2021. https://doi.org/10.1145/3472163.3472164

  30. Grover, L.K.: A fast quantum mechanical algorithm for database search. In: Proceedings of the Twenty-Eighth Annual ACM Symposium on Theory of Computing - STOC 1996. ACM Press (1996). https://doi.org/10.1145/237814.237866

  31. Günnemann, S.: Machine learning meets databases. Datenbank-Spektrum 17(1), 77–83 (2017). https://doi.org/10.1007/s13222-017-0247-8

    Article  Google Scholar 

  32. Gupta, C., Mehta, A., Dayal, U.: PQR: predicting query execution times for autonomous workload management. In: 2008 International Conference on Autonomic Computing, pp. 13–22. IEEE (2008)

    Google Scholar 

  33. Han, Y., et al.: Cardinality estimation in DBMS. Proc. VLDB Endowment 15(4), 752–765 (2021). https://doi.org/10.14778/3503585.3503586

  34. Harrow, A.W., Hassidim, A., Lloyd, S.: Quantum algorithm for linear systems of equations. Phys. Rev. Lett. 103(15) (2009). https://doi.org/10.1103/physrevlett.103.150502

  35. Hasan, R., Gandon, F.: A machine learning approach to SPARQL query performance prediction. In: 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), vol. 1, pp. 266–273. IEEE (2014)

    Google Scholar 

  36. Heitz, J., Stockinger, K.: Join query optimization with deep reinforcement learning algorithms. arXiv:1911.11689 (2019)

  37. Holland, J.H.: Genetic algorithms. Sci. Am. 267(1), 66–73 (1992)

    Article  Google Scholar 

  38. Horng, J.T., Kao, C.Y., Liu, B.J.: A genetic algorithm for database query optimization. In: Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence. IEEE (1994). https://doi.org/10.1109/icec.1994.349926

  39. Huang, H.Y., et al.: Power of data in quantum machine learning. Nat. Commun. 12(1) (2021)

    Google Scholar 

  40. Ioannidis, Y.E., Wong, E.: Query optimization by simulated annealing. In: Proceedings of the 1987 ACM SIGMOD International Conference on Management of Data - SIGMOD 1987. ACM Press (1987). https://doi.org/10.1145/38713.38722

  41. Johnson, M.W., et al.: Quantum annealing with manufactured spins. Nature 473(7346), 194–198 (11). https://doi.org/10.1038/nature10012

  42. Kadowaki, T., Nishimori, H.: Quantum annealing in the transverse Ising model. Phys. Rev. E 58(5), 5355 (1998)

    Article  Google Scholar 

  43. Kim, K., Jung, J., Seo, I., Han, W.S., Choi, K., Chong, J.: Learned cardinality estimation: an in-depth study. In: Proceedings of the 2022 International Conference on Management of Data, pp. 1214–1227 (2022)

    Google Scholar 

  44. Li, G., Zhou, X., Cao, L.: Machine learning for databases. In: The First International Conference on AI-ML-Systems. ACM, October 2021. https://doi.org/10.1145/3486001.3486248

  45. Liu, M., Zhang, F., Ma, Y., Pota, H.R., Shen, W.: Evacuation path optimization based on quantum ant colony algorithm. Adv. Eng. Inform. 30(3), 259–267 (2016)

    Article  Google Scholar 

  46. Luo, G., Naughton, J.F., Ellmann, C.J., Watzke, M.W.: Transaction reordering. Data Knowl. Eng. 69(1), 29–49 (2010)

    Article  Google Scholar 

  47. Marcus, R., Papaemmanouil, O.: Deep reinforcement learning for join order enumeration. In: Proceedings of the First International Workshop on Exploiting Artificial Intelligence Techniques for Data Management. ACM, June 2018. https://doi.org/10.1145/3211954.3211957

  48. Masum, M., et al.: Quantum machine learning for software supply chain attacks: how far can we go? In: 2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC), pp. 530–538. IEEE Computer Society, Los Alamitos, July 2022. https://doi.org/10.1109/COMPSAC54236.2022.00097

  49. Mirjalili, S., Lewis, A.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016). https://doi.org/10.1016/j.advengsoft.2016.01.008

    Article  Google Scholar 

  50. Moore, M., Narayanan, A.: Quantum-inspired computing. Department Computer Science, University Exeter, Exeter, UK (1995)

    Google Scholar 

  51. Rebentrost, P., Mohseni, M., Lloyd, S.: Quantum support vector machine for big data classification. Phys. Rev. Lett. 113, 130503 (2014). https://doi.org/10.1103/PhysRevLett.113.130503

    Article  Google Scholar 

  52. Ronagh, P.: The problem of dynamic programming on a quantum computer (2019). https://doi.org/10.48550/ARXIV.1906.02229. https://arxiv.org/abs/1906.02229

  53. Roy, S., Kot, L., Koch, C.: Quantum databases. In: Sixth Biennial Conference on Innovative Data Systems Research (CIDR), Asilomar, CA, USA (2013). www.cidrdb.org. http://cidrdb.org/cidr2013/Papers/CIDR13_Paper86.pdf

  54. Saggio, V., et al.: Experimental quantum speed-up in reinforcement learning agents. Nature 591(7849), 229–233 (2021). https://doi.org/10.1038/s41586-021-03242-7

  55. Sajjan, M., et al.: Quantum machine learning for chemistry and physics. Chem. Soc. Rev. 51(15), 6475–6573 (2022). https://doi.org/10.1039/d2cs00203e

  56. Schönberger, M., Scherzinger, S., Mauerer, W.: Applicability of quantum computing on database query optimization. In: Frühjahrstreffen Fachgruppe Datenbanken in Potsdam (Poster Presentation), March 2022

    Google Scholar 

  57. Selinger, P.G., Astrahan, M.M., Chamberlin, D.D., Lorie, R.A., Price, T.G.: Access path selection in a relational database management system. In: Proceedings of the 1979 ACM SIGMOD International Conference on Management of Data - SIGMOD 1979. ACM Press (1979). https://doi.org/10.1145/582095.582099

  58. Subaşı, Y., Somma, R.D., Orsucci, D.: Quantum algorithms for systems of linear equations inspired by adiabatic quantum computing. Phys. Rev. Lett. 122(6) (2019). https://doi.org/10.1103/physrevlett.122.060504

  59. Trummer, I., Koch, C.: Multiple query optimization on the d-wave 2x adiabatic quantum computer. Proc. VLDB Endowment 9(9), 648–659 (2016). https://doi.org/10.14778/2947618.2947621

  60. Trummer, I., Koch, C.: Solving the join ordering problem via mixed integer linear programming. In: Proceedings of the 2017 ACM International Conference on Management of Data. ACM, May 2017. https://doi.org/10.1145/3035918.3064039

  61. Wang, H., et al.: April: An automatic graph data management system based on reinforcement learning. In: Proceedings of the 29th ACM International Conference on Information and Knowledge Management, pp. 3465–3468 (2020)

    Google Scholar 

  62. Wang, H., Liu, J., Zhi, J., Fu, C.: The improvement of quantum genetic algorithm and its application on function optimization. Math. Problems Eng. 2013, 1–10 (2013). https://doi.org/10.1155/2013/730749

  63. Wang, L., Niu, Q., Fei, M.: A novel quantum ant colony optimization algorithm. In: Li, K., Fei, M., Irwin, G.W., Ma, S. (eds.) LSMS 2007. LNCS, vol. 4688, pp. 277–286. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-74769-7_31

    Chapter  Google Scholar 

  64. Woltmann, L., Hartmann, C., Thiele, M., Habich, D., Lehner, W.: Cardinality estimation with local deep learning models. In: Proceedings of the Second International Workshop on Exploiting Artificial Intelligence Techniques for Data Management - aiDM 2019. ACM Press (2019). https://doi.org/10.1145/3329859.3329875

  65. Woltmann, L., Olwig, D., Hartmann, C., Habich, D., Lehner, W.: PostCENN: PostgreSQL with machine learning models for cardinality estimation. Proc. VLDB Endowment 14(12), 2715–2718 (2021)

    Article  Google Scholar 

  66. Yu, X., Li, G., Chai, C., Tang, N.: Reinforcement learning with tree-LSTM for join order selection. In: 2020 IEEE 36th International Conference on Data Engineering (ICDE). IEEE, April 2020. https://doi.org/10.1109/icde48307.2020.00116

  67. Zhao, K., Yu, J.X., He, Z., Li, R., Zhang, H.: Lightweight and accurate cardinality estimation by neural network gaussian process. In: Proceedings of the 2022 International Conference on Management of Data, pp. 973–987 (2022)

    Google Scholar 

  68. Zhao, R., Wang, S.: A review of quantum neural networks: methods, models, dilemma. arXiv:2109.01840 (2021). https://doi.org/10.48550/ARXIV.2109.01840

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Acknowledgments

This work is funded by the German Federal Ministry of Education and Research within the funding program quantum technologies - from basic research to market - contract number 13N16090.

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Authors and Affiliations

  1. Institute of Information Systems (IFIS), University of Lübeck, Lübeck, Germany

    Sven Groppe, Jinghua Groppe, Umut Çalıkyılmaz & Tobias Winker

  2. University of Oklahoma, Norman, USA

    Le Gruenwal

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Correspondence to Sven Groppe .

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  1. VIT-AP University, Amrāvati, Andhra Pradesh, India

    Sachi Nandan Mohanty

  2. University of Oviedo, Oviedo, Spain

    Vicente Garcia Diaz

  3. Vardhaman College of Engineering, Hyderabad, India

    G. A. E. Satish Kumar

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Groppe, S., Groppe, J., Çalıkyılmaz, U., Winker, T., Gruenwal, L. (2023). Quantum Data Management and Quantum Machine Learning for Data Management: State-of-the-Art and Open Challenges. In: Nandan Mohanty, S., Garcia Diaz, V., Satish Kumar, G.A.E. (eds) Intelligent Systems and Machine Learning. ICISML 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 471. Springer, Cham. https://doi.org/10.1007/978-3-031-35081-8_20

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