Quantum Computing and Artificial Intelligence: The Synergy of Two Revolutionary Technologies

Authors

  • Ali Ahmadi Department of IT Management, Faculty of Management, Payam-e Noor University, Iraq

DOI:

https://doi.org/10.51983/ajes-2023.12.2.4118

Keywords:

Quantum Computing, Deep Learning, Artificial Intelligence, Natural Language Processing, Cryptography

Abstract

An important turning point in the history of technology and computation is the confluence of Quantum Computing and Artificial Intelligence (AI). Redefining the limits of what is possible, quantum computing delivers previously unheard-of computational capabilities by utilizing the special qualities of quantum physics. AI, on the other hand, has made remarkable strides in simulating human intelligence, particularly through deep learning and natural language processing. This article explores the profound synergy arising from the intersection of Quantum Computing and AI. It explores the advantages and possible uses of this fusion, including how it can revolutionize the way complicated issues in drug development, cryptography, optimization, and other scientific fields are resolved. Additionally, it scrutinizes the challenges and ethical considerations inherent in this powerful merger. As Quantum Computing and AI continue to evolve and mature, their interplay promises to reshape industries and unlock new frontiers, bringing to life possibilities that were once confined to the realm of science fiction. This article navigates the exciting journey of these two groundbreaking technologies and their combined potential to revolutionize our world.

References

B. Copeland, “Artificial Intelligence,” Encyclopedia Britannica, Mar. 31, 2023. [Online]. Available: https://www.britannica.com/technology/artificial-intelligence.

IBM, “What is Artificial Intelligence?” [Online]. Available: https://www.ibm.com/topics/artificial-intelligence.

C. M. Gevaert, M. Carman, B. Rosman, Y. Georgiadou, and R. Soden, “Fairness and Accountability of AI in Disaster Risk Management.”

A. Ahmadi, “ChatGPT: Exploring the Threats and Opportunities of Artificial Intelligence in the Age of Chatbots,” Asian Journal of Computer Science and Technology, vol. 12, no. 1, pp. 25-30, 2023. [Online]. Available: https://doi.org/10.51983/ajcst-2023.12.1.3567.

M. Martonosi and M. Roetteler, “Next steps in quantum computing: Computer science’s role,” arXiv preprint arXiv:1903.10541, 2019.

M. Losert, “How Quantum Computing Could Change the World,” DUJS, vol. 6, 2015.

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436-444, 2015.

Y. Bengio, Y. Lecun, and G. Hinton, “Deep learning for AI,” Communications of the ACM, vol. 64, no. 7, pp. 58-65, 2021.

J. Schmidhuber, “Deep learning in neural networks: An overview,” Neural Networks, vol. 61, pp. 85-117, 2015.

S. Bird, E. Klein, and E. Loper, Natural language processing with Python: Analyzing text with the natural language toolkit. O’Reilly Media, Inc., 2009.

J. Devlin, M. W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” arXiv preprint arXiv:1810.04805, 2018.

B. Schneier, “Algorithm Types and Modes,” in Applied Cryptography, Second Edition: Protocols, Algorithms, and Source Code in C, pp. 189-211.

C. Paar and J. Pelzl, Understanding cryptography: A textbook for students and practitioners. Springer Science & Business Media, 2009.

G. Alagic et al., “Status report on the third round of the NIST post-quantum cryptography standardization process,” US Department of Commerce, NIST, 2022.

M. A. Nielsen and I. L. Chuang, Quantum computation and quantum information. Cambridge University Press, 2010.

J. Biamonte et al., “Quantum machine learning,” Nature, vol. 549, no. 7671, pp. 195-202, 2017.

J. Preskill, “Quantum computing in the NISQ era and beyond,” Quantum, vol. 2, pp. 79, 2018.

D. P. García, J. Cruz-Benito, and F. J. García-Peñalvo, “Systematic literature review: Quantum machine learning and its applications,” arXiv preprint arXiv:2201.04093, 2022.

P. Agarwal and M. Alam, “Exploring Quantum Computing to Revolutionize Big Data Analytics for Various Industrial Sectors,” in Big Data Analytics, pp. 113-130, Auerbach Publications, 2021.

A. Daley, I. Cirac, and P. Zoller, “The Development of Quantum Hardware for Quantum Computing,” in Disappearing Architecture, pp. 62-76, Birkhäuser Basel, 2005.

E. K. Cortez, J. R. Bambauer, and S. Guha, “A Quantum Policy and Ethics Roadmap,” SSRN 4507090, 2023.

H. Alyami, et al., “The evaluation of software security through quantum computing techniques: A durability perspective,” Applied Sciences, vol. 11, no. 24, pp. 11784, 2021.

A. Rayhan and S. Rayhan, “Quantum Computing and AI: A Quantum Leap in Intelligence,” 2023.

V. Chauhan, et al., “Quantum Computers: A Review on How Quantum Computing Can Boom AI,” in 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), pp. 559-563, IEEE, April 2022.

M. Schuld, I. Sinayskiy, and F. Petruccione, “An introduction to quantum machine learning,” Contemporary Physics, vol. 56, no. 2, pp. 172-185, 2015.

B. M. Terhal and D. P. DiVincenzo, “Adaptive quantum computation, constant depth quantum circuits and Arthur-Merlin games,” arXivpreprintquant-ph/0205133, 2002.

H. S. Zhong et al., “Quantum computational advantage using photons,” Science, vol. 370, no. 6523, pp. 1460-1463, 2020.

E. Schrödinger, “Discussion of probability relations between separated systems,” Mathematical Proceedings of the Cambridge Philosophical Society, vol. 31, no. 4, pp. 555-563, Oct. 1935.

I. L. Chuang and M. A. Nielsen, “Prescription for experimental determination of the dynamics of a quantum black box,” Journal of Modern Optics, vol. 44, no. 11-12, pp. 2455-2467, 1997.

M. A. Nielsen, “Optical quantum computation using cluster states,” Physical review letters, vol. 93, no. 4, p. 040503, 2004.

P. W. Shor, “Algorithms for quantum computation: discrete logarithms and factoring,” in Proceedings 35th Annual Symposium on Foundations of Computer Science, IEEE, pp. 124-134, Nov. 1994.

S. J. Devitt, W. J. Munro, and K. Nemoto, “Quantum error correction for beginners,” Reports on Progress in Physics, vol. 76, no. 7, pp. 076001, 2013.

F. Arute, et al., “Quantum supremacy using a programmable superconducting processor,” Nature, vol. 574, no. 7779, pp. 505-510, 2019.

S. Boixo, et al., “Characterizing quantum supremacy in near-term devices,” Nature Physics, vol. 14, no. 6, pp. 595-600, 2018.

J. Preskill, “Quantum computing and the entanglement frontier,” arXiv preprint arXiv:1203.5813, 2012.

A. M. Turing, “Computing machinery and intelligence,” Springer Netherlands, pp. 23-65, 2009.

M. J. Apter, “Machines Who Think: A Personal Inquiry into the History and Prospects of Artificial Intelligence by Pamela McCorduck,” Leonardo, vol. 15, no. 3, pp. 242-242, 1982.

T. Hastie, R. Tibshirani, J. H. Friedman, and J. H. Friedman, The elements of statistical learning: data mining, inference, and prediction. New York: Springer, 2009, vol. 2, pp. 1-758.

S. J. Russell and P. Norvig, Artificial intelligence: a modern approach. London, 2010.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Communications of the ACM, vol. 60, no. 6, pp. 84-90, 2017.

B. Mann et al., “Language models are few-shot learners,” arXiv preprint arXiv:2005.14165, 2020.

S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation, vol. 9, no. 8, pp. 1735-1780, 1997.

D. Chen and H. Zhao, “Data security and privacy protection issues in cloud computing,” in 2012 International Conference on Computer Science and Electronics Engineering, IEEE, vol. 1, pp. 647-651, Mar. 2012.

A. Géron, Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow. “O’Reilly Media, Inc.”, 2022.

S. Raschka and V. Mirjalili, Python machine learning: Machine learning and deep learning with python, Scikit-Learn, and TensorFlow (Second edition ed.). 3, 2017.

B. Zhou, J. Pei, and W. Luk, “A brief survey on anonymization techniques for privacy-preserving publishing of social network data,” ACM Sigkdd Explorations Newsletter, vol. 10, no. 2, pp. 12-22, 2008.

P. Rebentrost, M. Mohseni, and S. Lloyd, “Quantum support vector machine for big data classification,” Physical review letters, vol. 113, no. 13, pp. 130503, 2014.

V. Dunjko, J. M. Taylor, and H. J. Briegel, “Quantum-enhanced machine learning,” Physical review letters, vol. 117, no. 13, pp. 130501, 2016.

K. Mitarai, M. Negoro, M. Kitagawa, and K. Fujii, “Quantum circuit learning,” Physical Review A, vol. 98, no. 3, pp. 032309, 2018.

A. Ahmadi, “Artificial intelligence and mental disorders: chicken-or-the-egg issue,” Journal of Biological Studies, vol. 6, no. 1, pp. 7-18, 2023. [Online]. Available: https://onlinejbs.com/index.php/jbs/article /view/7751.

M. M. Wilde, Quantum information theory. Cambridge University Press, 2013.

V. Vedral, Decoding reality: The universe as quantum information. Oxford University Press, 2018.

B. Mishra and A. Samanta, “Quantum Transfer Learning Approach for Deepfake Detection,” Sparklinglight Transactions on Artificial Intelligence and Quantum Computing (STAIQC), vol. 2, no. 1, pp. 17-27, 2022.

N. R. Byreddy, “DeepFake Videos Detection Using Machine Learning,” Ph.D. dissertation, Dublin, National College of Ireland, 2019.

I. Kassal, et al., “Simulating chemistry using quantum computers,” Annual review of physical chemistry, vol. 62, pp. 185-207, 2011.

M. Reiher et al., “Elucidating reaction mechanisms on quantum computers,” Proceedings of the National Academy of Sciences, vol. 114, no. 29, pp. 7555-7560, 2017.

S. Rose, “The coming explosion of silent weapons,” Naval War College Review, vol. 42, no. 3, pp. 6-29, 1989.

S. A. A. Shah, N. Algeelani, and N. Al-Sammarraie, “Quantum-AI empowered Intelligent Surveillance: Advancing Public Safety Through Innovative Contraband Detection,” arXiv preprint arXiv:2309.03231, 2023.

J. Preskill, “Quantum Computing in the NISQ era and beyond,” Quantum, vol. 2, pp. 79, 2018. [Online]. Available: https://arxiv.org/abs/1801.00862.

E. Farhi et al., “Quantum computation by adiabatic evolution,” arXiv preprint quant-ph/0001106, 2000.

M. Benedetti, J. Realpe-Gómez, R. Biswas, and A. Perdomo-Ortiz, “Estimation of effective temperatures in quantum annealers for sampling applications: A case study with possible applications in deep learning,” Physical Review A, vol. 94, no. 2, pp. 022308, 2016.

G. Torlai and R. G. Melko, “Machine-learning quantum states in the NISQ era,” Annual Review of Condensed Matter Physics, vol. 11, pp. 325-344, 2020.

F. Collins, The Language of Life: DNA and the Revolution in Personalized Medicine. Profile Books, 2010.

F. S. Collins and H. Varmus, “A new initiative on precision medicine,” New England Journal of Medicine, vol. 372, no. 9, pp. 793-795, 2015.

E. A. Ashley, “Towards precision medicine,” Nature Reviews Genetics, vol. 17, no. 9, pp. 507-522, 2016.

M. V. Relling and W. E. Evans, “Pharmacogenomics in the clinic,” Nature, vol. 526, no. 7573, pp. 343-350, 2015.

K. R. Crews et al., “Clinical Pharmacogenetics Implementation Consortium guidelines for cytochrome P450 2D6 genotype and codeine therapy: 2014 update,” Clinical Pharmacology & Therapeutics, vol. 95, no. 4, pp. 376-382, 2014.

Y. Cao et al., “Quantum chemistry in the age of quantum computing,” Chemical Reviews, vol. 119, no. 19, pp. 10856-10915, 2019.

C. Lee et al., “Entanglement-based quantum communication secured by nonlocal dispersion cancellation,” Physical Review A, vol. 90, no. 6, pp. 062331, 2014.

J. Yin et al., “Entanglement-based secure quantum cryptography over 1,120 kilometers,” Nature, vol. 582, no. 7813, pp. 501-505, 2020.

M. Schuld and N. Killoran, “Quantum machine learning in feature Hilbert spaces,” Physical Review Letters, vol. 122, no. 4, pp. 040504, 2019.

M. Schuld, M. Fingerhuth, and F. Petruccione, “Implementing a distance-based classifier with a quantum interference circuit,” Europhysics Letters, vol. 119, no. 6, p. 60002, 2017.

S. Lloyd, M. Mohseni, and P. Rebentrost, “Quantum algorithms for supervised and unsupervised machine learning,” arXiv preprint arXiv:1307.0411, 2013.

S. Lloyd et al., “Quantum embeddings for machine learning,” arXiv preprint arXiv:2001.03622, 2020.

V. Dunjko and P. Wittek, “A non-review of quantum machine learning: trends and explorations,” Quantum Views, vol. 4, pp. 32, 2020.

L. Jiao et al., “Quantum-inspired immune clonal algorithm for global optimization,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 38, no. 5, pp. 1234-1253, 2008.

N. Gisin et al., “Quantum cryptography,” Reviews of Modern Physics, vol. 74, no. 1, p. 145, 2002.

P. W. Shor and J. Preskill, “Simple proof of security of the BB84 quantum key distribution protocol,” Physical Review Letters, vol. 85, no. 2, pp. 441, 2000.

A. K. Ekert, “Quantum cryptography based on Bell’s theorem,” Physical Review Letters, vol. 67, no. 6, pp. 661, 1991.

D. Bouwmeester et al., “Experimental quantum teleportation,” Nature, vol. 390, no. 6660, pp. 575-579, 1997.

L. Jiang, J. M. Taylor, and M. D. Lukin, “Fast and robust approach to long-distance quantum communication with atomic ensembles,” Physical Review A, vol. 76, no. 1, pp. 012301, 2007.

J. Yin et al., “Satellite-based entanglement distribution over 1200 kilometers,” Science, vol. 356, no. 6343, pp. 1140-1144, 2017.

M. Krenn et al., “Quantum communication with photons,” Optics in our Time, vol. 18, p. 455, 2016.

K. Günthner et al., “Quantum-limited measurements of optical signals from a geostationary satellite,” Optica, vol. 4, no. 6, pp. 611-616, 2017.

C. Hughes et al., Quantum Computing for the Quantum Curious. Springer Nature, 2021, p. 150.

S. Pirandola et al., “Advances in quantum cryptography,” Advances in Optics and Photonics, vol. 12, no. 4, pp. 1012-1236, 2020.

R. Horodecki et al., “Quantum entanglement,” Reviews of Modern Physics, vol. 81, no. 2, p. 865, 2009.

N. Diakopoulos, “Accountability in algorithmic decision making,” Communications of the ACM, vol. 59, no. 2, pp. 56-62, 2016.

D. Hadfield-Menell et al., “Cooperative inverse reinforcement learning,” in Advances in Neural Information Processing Systems, vol. 29, 2016.

A. Bouland et al., “Quantum supremacy and the complexity of random circuit sampling,” arXiv preprint arXiv:1803.04402, 2018.

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Published

02-11-2023

How to Cite

Ahmadi, A. (2023). Quantum Computing and Artificial Intelligence: The Synergy of Two Revolutionary Technologies. Asian Journal of Electrical Sciences, 12(2), 15–27. https://doi.org/10.51983/ajes-2023.12.2.4118