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Quantum AI: Achievements and Challenges in the Interplay of Quantum Computing and Artificial Intelligence

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Ambient Intelligence—Software and Applications—13th International Symposium on Ambient Intelligence (ISAmI 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 603))

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Abstract

In recent times, Quantum Computing (QC) is receiving growing attention, thanks to the enormous advances in the construction of operational quantum computers, quantum materials and quantum cryptography. Given the advances in the physical construction and the scaling up of quantum computers, it is now necessary to foster the creation of quantum algorithms and methods that are adapted to such computers and that make the most of their intrinsic computational and communication capabilities. In the era of Big Data, some of the most computationally demanding tasks fall into the field of Artificial Intelligence (AI), including tasks that are currently computationally intractable due to physical limitations. The intrinsic parallelism, computational efficiency, and representational power offered by QC make for an excellent alternative to binary computers, holding the promise of enhanced AI models. This novel Quantum Artificial Intelligence (QAI) concept will result in the detection of patterns that classical AI algorithms are unable to identify, and in the time reduction of several orders of magnitude. In this review, we describe the scientific progresses in the confluence of AI and QC. We start by presenting both areas, basic concepts and the timeline of most significant advances in the history of AI and QC, to then focus on existing research made in the bidirectional approaches of QC benefiting from AI and AI benefiting from QC. Finally, we describe future avenues of research for the QAI incipient scientific area, and conclude.

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References

  1. Ahmed, H., Fosong, A.: Towards quantum-secure authentication and key agreement via abstract multi-agent interaction. In: Practical Applications of Agents and Multi-Agent Systems, pp. 14–26. Springer, Heidelberg (2021)

    Google Scholar 

  2. Albash, T., Lidar, D.A.: Adiabatic quantum computation. Rev. Modern Phys. 90(1), 015002 (2018) (number: 1 Publisher: APS)

    Google Scholar 

  3. Alonso, R.S., Prieto, J., García, O., Corchado, J.M.: Collaborative learning via social computing. Front. IT Electronic Eng. 20(2), 265–282 (2019) (number: 2 Publisher: Springer)

    Google Scholar 

  4. Alonso, R.S., Sittón-Candanedo, I., Casado-Vara, R., Prieto, J., Corchado, J.M.: Deep reinforcement learning for the management of software-defined networks and network function virtualization in an edge-IoT architecture. Sustainability 12(14), 5706 (2020) (number: 14 Publisher: Multidisciplinary Digital Publishing Institute)

    Google Scholar 

  5. Amin, M.H., Andriyash, E., Rolfe, J., Kulchytskyy, B., Melko, R.: Quantum boltzmann machine. Phys. Rev. X 8(2), 021050 (2018) (number: 2 Publisher: APS)

    Google Scholar 

  6. Arute et al.: Quantum supremacy using a programmable superconducting processor. Nature 574(7779), 505–510 (2019) (Nature Publishing Group)

    Google Scholar 

  7. Benedetti, R., Biswas, P.: 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 (2016) (number 2, APS)

    Google Scholar 

  8. Biamonte, W., Pancotti, R., Wiebe, L.: Quantum machine learning. Nature 549(7671), 195–202 (2017) (Nature Publishing Group)

    Google Scholar 

  9. Broughton et al.: Tensorflow Quantum: A Software Framework for Quantum Machine Learning. arXiv:2003.02989 (2020)

  10. Campbell, E., Khurana, A., Montanaro, A.: Applying quantum algorithms to constraint satisfaction problems. Quantum 3, 167 (2019) (publisher: Verein zur Förderung des Open Access Publizierens in den Quantenwissenschaften)

    Google Scholar 

  11. Cardoso, F.P.: Automated planning and BDI agents: a case study. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 52–63. Springer, Heidelberg (2021)

    Google Scholar 

  12. Carneiro, D., Silva, F., Guimarães, M., Sousa, D., Novais, P.: Explainable intelligent environments. In: International Symposium on Ambient Intelligence, pp. 34–43. Springer, Heidelberg (2020)

    Google Scholar 

  13. Chow, J., Dial, O., Gambetta, J.: IBM Quantum Breaks the 100-qubit Processor Barrier (2021)

    Google Scholar 

  14. Costa, A., Novais, P., Corchado, J.M., Neves, J.: Increased performance and better patient attendance in an hospital with the use of smart agendas. Logic J. IGPL 20(4), 689–698 (2012) (Oxford University Press)

    Google Scholar 

  15. Davenport, T.H., Barth, P., Bean, R.: How ‘big data’ is different. In: MIT Sloan Management Review (2012) (Publisher: MIT Sloan Management Review)

    Google Scholar 

  16. Debnath et al.: Demonstration of a small programmable quantum computer with atomic qubits. Nature 536(7614), 63–66 (2016)

    Google Scholar 

  17. Djezzar et al.: Quorum sensing digital simulations for the emergence of scalable and cooperative artificial networks. IJAIML 9(1), 13–34 (2019)

    Google Scholar 

  18. Dong, C., Li, T.: Quantum reinforcement learning. IEEE Trans. Syst. Man Cybern. Part B (Cybernetics) 38(5), 1207–1220 (2008)

    Google Scholar 

  19. Ezhov, A.A., Ventura, D.: Quantum neural networks. In: Future Directions for Intelligent Systems and Information Sciences, pp. 213–235. Springer, Heidelberg (2000)

    Google Scholar 

  20. Faia, P., Abrishambaf, F., Vale, C.: Case based reasoning with expert system and swarm intelligence to determine energy reduction in buildings energy management. Energy Build. 155, 269–281 (2017) (Elsevier)

    Google Scholar 

  21. Fatima et al.: Enhancing Performance of a Deep Neural Network: A Comparative Analysis of Optimization Algorithms (2020) (ediciones Universidad de Salamanca)

    Google Scholar 

  22. Fernández Pérez, I., Boumaza, A., Charpillet, F.: Decentralized innovation marking for neural controllers in embodied evolution. In: Annual Conference on Genetic and Evolutionary Computation, pp. 161–168 (2015)

    Google Scholar 

  23. Feynman, R.P., et al.: Simulating physics with computers. Int. J. Theor. Phys. 21(6/7) (1982) (Number: 6/7)

    Google Scholar 

  24. Finnila et al.: Quantum annealing: a new method for minimizing multidimensional functions. Chem. Phys. Lett. 219(5–6), 343–348 (1994) (Elsevier)

    Google Scholar 

  25. Friedman, J.R., Patel, V., Chen, W., Tolpygo, S., Lukens, J.E.: Quantum superposition of distinct macroscopic states. Nature 406(6791), 43–46 (2000)

    Article  Google Scholar 

  26. García, O., Alonso, R.S., Prieto, J., Corchado, J.M.: Energy efficiency in public buildings through context-aware social computing. Sensors 17(4), 826 (2017) (number: 4 Publisher: Multidisciplinary Digital Publishing Institute)

    Google Scholar 

  27. Gazafroudi, C., Keane, S.: Decentralised flexibility management for EVs. IET Renew. Power Gener. 13(6), 952–960 (2019) (iET)

    Google Scholar 

  28. Giovannetti, V., Lloyd, S., Maccone, L.: Quantum random access memory. Phys. Rev. Lett. 100(16), 160501 (2008) (number: 16 Publisher: APS)

    Google Scholar 

  29. Gisin, N., Ribordy, G., Tittel, W., Zbinden, H.: Quantum cryptography. Rev. Modern Phys. 74(1), 145 (2002) (number: 1 Publisher: APS)

    Google Scholar 

  30. Grifoni, M., Hänggi, P.: Driven quantum tunneling. Phys. Rep. 304(5-6), 229–354 (1998) (number: 5-6 Publisher: Elsevier)

    Google Scholar 

  31. Grover, L.K.: A fast quantum mechanical algorithm for database search. In: 28th Annual ACM Symposium on Theory of Computing, pp. 212–219 (1996)

    Google Scholar 

  32. Gupta, M., et al.: Neural Network Based Epileptic EEG Detection and Classification (2020). Ediciones Universidad de Salamanca (España)

    Google Scholar 

  33. Hager, G., Wellein, G.: Introduction to High Performance Computing for Scientists and Engineers. CRC Press (2010)

    Google Scholar 

  34. Horn, D., Gottlieb, A.: Algorithm for data clustering in pattern recognition problems based on quantum mechanics. Phys. Rev. Lett. 88(1), 018702 (2001)

    Article  Google Scholar 

  35. Horodecki, R., Horodecki, P., Horodecki, M., Horodecki, K.: Quantum entanglement. Rev. Modern Phys. 81(2), 865 (2009) (number: 2 Publisher: APS)

    Google Scholar 

  36. Hsu, F.H.: IBM’s deep blue chess grandmaster chips. IEEE Micro 19(2), 70–81 (1999) (number: 2 Publisher: IEEE)

    Google Scholar 

  37. Huang, B.: Mohseni, Babbush, Boixo, Neven, McClean: power of data in quantum machine learning. Nat. Commun. 12(1), 2631 (2021)

    Article  Google Scholar 

  38. Jasim, Y.A.: High-performance deep learning to detection and tracking tomato plant leaf predict disease and expert systems. ADCAIJ: Adv. Distributed Comput. Artif. Intell. J. 10(2) (2021)

    Google Scholar 

  39. Jones: The quantum company: D-Wave pioneering a way of making quantum computers but also courting controversy. Nature 498(7454), 286–289 (2013)

    Google Scholar 

  40. Lamata et al.: Quantum autoencoders via quantum adders with genetic algorithms. Quantum Sci. Technol. 4(1), 014007 (2018)

    Google Scholar 

  41. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015) (number: 7553 Publisher: Nature Publishing Group)

    Google Scholar 

  42. Li, S., Liu, C.: Approximate Gaussian conjugacy: parametric recursive filtering under nonlinearity, multimodality, uncertainty, and constraint, and beyond. Front. IT Electronic Eng. 18(12), 1913–1939 (2017) (Springer)

    Google Scholar 

  43. Lloyd, S., Mohseni, M., Rebentrost, P.: Quantum principal component analysis. Nat. Phys. 10(9), 631–633 (2014) (number 9, Nature Publishing Group)

    Google Scholar 

  44. Márquez, S., Mora, S., Herrera, J., Roncero, A., Corchado, J.M.: Intelligent dolls and robots for the treatment of elderly people with dementia. Adv. Distributed Comput. Artif. Intell. J. 9(1), 99–112 (2020)

    Article  Google Scholar 

  45. McClean, H.: Quantum Machine Learning and the Power of Data (2021). https://ai.googleblog.com/2021/06/quantum-machine-learning-and-power-of.html

  46. Mezquita, A., Casado-Vara, P., Corchado: A review of k-NN algorithm based on classical and quantum machine learning. In: International Symposium on Distributed Computing and AI (DCAI), pp. 189–198. Springer, Heidelberg (2020)

    Google Scholar 

  47. Mishra, D.: Brain Inspired Computing Approach for the Optimization of the Thin Film Thickness of Polystyrene on the Glass Substrates. arXiv:2107.12156 (2021)

  48. Moor, J.: The Dartmouth College artificial intelligence conference: the next fifty years. AI Mag. 27(4), 87–87 (2006) (number: 4)

    Google Scholar 

  49. Mugunthan, R., Kagal: BlockFLow: decentralized, privacy-preserving, and accountable federated machine learning. In: International Congress on Blockchain and Applications, pp. 233–242. Springer, Heidelberg (2021)

    Google Scholar 

  50. Mäkelä, H., Messina, A.: N-qubit states as points on the Bloch sphere. Physica Scripta 2010(T140), 014054 (2010) (number: T140 Publisher: IOP Publishing)

    Google Scholar 

  51. Möttönen, M., et al.: Quantum circuits for general multiqubit gates. Phys. Rev. Lett. 93(13), 130502 (2004) (number 13, APS)

    Google Scholar 

  52. Najafabadi, M.M., Villanustre, F., Khoshgoftaar, T.M., Seliya, N., Wald, R., Muharemagic, E.: Deep learning applications and challenges in big data analytics. J. Big Data 2(1), 1–21 (2015) (number: 1 Publisher: Springer)

    Google Scholar 

  53. Nguyen, T.T., Hatua, A., Sung, A.H.: Blockchain approach to solve collective decision making problems for swarm robotics. In: International Congress on Blockchain and Applications, pp. 118–125. Springer, Heidelberg (2019)

    Google Scholar 

  54. Patil, N., Rigoli, K., Richardson: Dynamical perceptual-motor primitives for better deep reinforcement learning agents. In: Practical Applications of Agents and Multi-Agent Systems, pp. 176–187. Springer, Heidelberg (2021)

    Google Scholar 

  55. Perdrix, S.: Quantum patterns and types for entanglement and separability. Electronic Notes Theor. Comput. Sci. 170, 125–138 (2007) (Elsevier)

    Google Scholar 

  56. Prieto, M.: Win: context-aided inertial navigation via belief condensation. IEEE Trans. Signal Process. 64(12), 3250–3261 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  57. Rempe, G., Walther, H., Klein, N.: Observation of quantum collapse and revival in a one-atom maser. Phys. Rev. Lett. 58(4), 353 (1987) (number: 4, APS)

    Google Scholar 

  58. Riedel, K., Zoller, M., Calarco: Europe’s quantum flagship initiative. Quantum Sci. Technol. 4(2), 020501 (2019) (number 2, IOP Publishing)

    Google Scholar 

  59. Sasaki, M., Carlini, A., Jozsa, R.: Quantum template matching. Phys. Rev. A 64(2), 022317 (2001) (number: 2 Publisher: APS)

    Google Scholar 

  60. Schliemann, J., Khaetskii, A.V., Loss, D.: Spin decay and quantum parallelism. Phys. Rev. B 66(24), 245303 (2002) (number: 24 Publisher: APS)

    Google Scholar 

  61. Schlosshauer: Quantum decoherence. Phys. Rep. 831, 1–57 (2019) (Elsevier)

    Google Scholar 

  62. Schuld, M., Petruccione, F.: Supervised Learning with Quantum Computers, vol. 17. Springer, Heidelberg (2018)

    Google Scholar 

  63. Schuld, M., Sinayskiy, I., Petruccione, F.: An introduction to quantum machine learning. Contemporary Phys. 56(2), 172–185 (2015) (number 1, Taylor & Francis)

    Google Scholar 

  64. Shoeibi, K., Corchado: Artificial intelligence as a way of overcoming visual disorders: damages related to visual cortex, optic nerves and eyes. In: Distributed Computing and Artificial Intelligence, pp. 183–187. Springer, Heidelberg (2019)

    Google Scholar 

  65. Shor: Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer. SIAM J. Comput. 26(5), 1484–1509 (1997)

    Google Scholar 

  66. Silva et al.: Classification of chest diseases using deep learning. In: Distributed Computing and Artificial Intelligence, pp. 152–158. Springer, Heidelebrg (2020)

    Google Scholar 

  67. Sinanc, D., Demirezen, U., Sağıroğlu, S., et al.: Explainable Credit Card Fraud Detection with Image Conversion (2021) (ediciones Universidad de Salamanca)

    Google Scholar 

  68. Tadepalli, T.: COVID-19 early symptom prediction using blockchain and machine learning. In: International Congress on Blockchain and Applications, pp. 243–251. Springer, Heidelberg (2021)

    Google Scholar 

  69. Turing, A.M.: Intelligent Machinery (1948)

    Google Scholar 

  70. Von Neumann, J.: First draft of a report on the EDVAC. IEEE Ann. Hist. Comput. 15(4), 27–75 (1993) (number: 4 Publisher: IEEE)

    Google Scholar 

  71. Yigitcanlar, K., Regona, R., Rowan, R., Desouza, C., Mehmood, L.: Artificial intelligence technologies and related urban planning and development concepts: how are they perceived and utilized in Australia? J. Open Innov.: Technol. Market Complexity 6(4), 187 (2020) (mDPI)

    Google Scholar 

  72. Zhong et al.: Quantum computational advantage using photons. Science 370(6523), 1460–1463 (2020)

    Google Scholar 

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Acknowledgements

This work has been supported by the project “XAI - XAI - Sistemas Inteligentes Auto Explicativos creados con Módulos de Mezcla de Expertos”, ID SA082P20, financed by Junta Castilla y León, Consejería de Educación, and FEDER funds.

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Authors and Affiliations

  1. BISITE Research Group, University of Salamanca, Salamanca, Spain

    Iñaki Fernández Pérez, Fernando de la Prieta, Sara Rodríguez-González, Juan M. Corchado & Javier Prieto

  2. AIR Institute, IoT Digital Innovation Hub, Salamanca, Spain

    Juan M. Corchado & Javier Prieto

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  1. Iñaki Fernández Pérez
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  2. Fernando de la Prieta
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  3. Sara Rodríguez-González
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  4. Juan M. Corchado
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  5. Javier Prieto
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Correspondence to Iñaki Fernández Pérez .

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Editors and Affiliations

  1. Universitat Politècnica de València, Valencia, Valencia, Spain

    Vicente Julián

  2. ISEP/GECAD, Porto, Portugal

    João Carneiro

  3. Científico Universidad de Salamanca, AIR Institute Villamayor, Parque, Salamanca, Spain

    Ricardo S. Alonso

  4. Edificio I+D+i (24.2), University of Salamanca, Salamanca, Salamanca, Spain

    Pablo Chamoso

  5. Departamento de Informática, University of Minho, Braga, Portugal

    Paulo Novais

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Fernández Pérez, I., Prieta, F.d.l., Rodríguez-González, S., Corchado, J.M., Prieto, J. (2023). Quantum AI: Achievements and Challenges in the Interplay of Quantum Computing and Artificial Intelligence. In: Julián, V., Carneiro, J., Alonso, R.S., Chamoso, P., Novais, P. (eds) Ambient Intelligence—Software and Applications—13th International Symposium on Ambient Intelligence. ISAmI 2022. Lecture Notes in Networks and Systems, vol 603. Springer, Cham. https://doi.org/10.1007/978-3-031-22356-3_15

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