Machine Learning for Quantum Computing Bridging the Gap between AI and Quantum Algorithms
Keywords:
Machine Learning, Quantum Computing, Artificial Intelligence, Quantum Algorithms, Hybrid Approaches.Abstract
This study explains how machine learning techniques are applied to enhance quantum algorithms and examines the interplay between machine learning and quantum computing. It explores quantum data analysis, quantum machine learning, and hybrid quantum-classical techniques, emphasizing their contributions to bridging the gap between artificial intelligence and quantum algorithms. Additionally, it analyzes how quantum data production, quantum-assisted optimization, and quantum neural networks could influence the direction of AI-quantum integration in the future.
Downloads
References
S. K. Baduge et al., “Artificial intelligence and smart vision for building and construction 4.0: Machine and deep learning methods and applications,” Automation in Construction, vol. 141. 2022. doi: 10.1016/j.autcon.2022.104440.
M. Tholkapiyan, S. Ramadass, J. Seetha, A. Ravuri, S. S. S, and S. Gore, “Examining the Impacts of Climate Variability on Agricultural Phenology: A Comprehensive Approach Integrating Geoinformatics, Satellite Agrometeorology, and Artificial Intelligence,” Int. J. Intell. Syst. Appl. Eng., vol. 11, no. 6s, pp. 592–598, 2023, Accessed: Aug. 18, 2023. [Online]. Available: https://ijisae.org/index.php/IJISAE/article/view/2891
D. Bzdok, T. E. Nichols, and S. M. Smith, “Towards algorithmic analytics for large-scale datasets,” Nature Machine Intelligence, vol. 1, no. 7. pp. 296–306, 2019. doi: 10.1038/s42256-019-0069-5.
R. de Wolf, “The potential impact of quantum computers on society,” Ethics Inf. Technol., vol. 19, no. 4, pp. 271–276, Dec. 2017, doi: 10.1007/s10676-017-9439-z.
The Physics of Quantum Information. Springer Berlin Heidelberg, 2000. doi: 10.1007/978-3-662-04209-0.
A. Jadhav, A. Rasool, and M. Gyanchandani, “Quantum Machine Learning: Scope for real-world problems,” in Procedia Computer Science, 2022, pp. 2612–2625. doi: 10.1016/j.procs.2023.01.235.
T. M. Khan and A. Robles-Kelly, “Machine Learning: Quantum vs Classical,” IEEE Access, vol. 8, pp. 219275–219294, 2020, doi: 10.1109/ACCESS.2020.3041719.
A. Avkhadiev, P. E. Shanahan, and R. D. Young, “Accelerating Lattice Quantum Field Theory Calculations via Interpolator Optimization Using Noisy Intermediate-Scale Quantum Computing,” Phys. Rev. Lett., vol. 124, no. 8, p. 80501, Feb. 2020, doi: 10.1103/PhysRevLett.124.080501.
R. M. Abd El-Aziz, A. I. Taloba, and F. A. Alghamdi, “Quantum Computing Optimization Technique for IoT Platform using Modified Deep Residual Approach,” Alexandria Eng. J., vol. 61, no. 12, pp. 12497–12509, 2022, doi: 10.1016/j.aej.2022.06.029.
M. Jamshidi, O. Moztarzadeh, A. Jamshidi, A. Abdelgawad, A. S. El-Baz, and L. Hauer, “Future of Drug Discovery: The Synergy of Edge Computing, Internet of Medical Things, and Deep Learning,” Futur. Internet, vol. 15, no. 4, 2023, doi: 10.3390/fi15040142.
S. Gore, G. S. P. S. Dhindsa, S. Gore, N. S. Jagtap, and U. Nanavare, “Recommendation of Contemporary Fashion Trends via AI-Enhanced Multimodal Search Engine and Blockchain Integration,” in 2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC), IEEE, Jul. 2023, pp. 1676–1682. doi: 10.1109/ICESC57686.2023.10193587.
R. Josphineleela et al., “Exploration Beyond Boundaries: AI-Based Advancements in Rover Robotics for Lunar Missions Space Like Chandrayaan,” ijisae.orgR Josphineleela, S Periasamy, N Krishnaveni, DS Prasad, BV Rao, MJ Garde, S GoreInternational J. Intell. Syst. Appl. Eng. 2023•ijisae.org, vol. 2023, no. 10s, Accessed: Oct. 04, 2023. [Online]. Available: https://www.ijisae.org/index.php/IJISAE/article/view/3318
Z. Abohashima, M. Elhosen, E. H. Houssein, and W. M. Mohamed, “Classification with Quantum Machine Learning: A Survey,” arxiv.org, 2020, Accessed: Oct. 04, 2023. [Online]. Available: https://arxiv.org/abs/2006.12270
H. Primas and A. Shimony, “Chemistry, Quantum Mechanics and Reductionism: Perspectives in Theoretical Chemistry,” Am. J. Phys., vol. 51, no. 12, pp. 1159–1160, 1983, doi: 10.1119/1.13329.
Y. Wang, “Quantum computation and quantum information,” Stat. Sci., vol. 27, no. 3, pp. 373–394, 2012, doi: 10.1214/11-STS378.
P. Malik, “Enhancing Feynman’s Quantum Computational Positioning to Inject New Possibility into the Foundations of the Quantum Computing Industry,” in 2022 IEEE 13th Annual Information Technology, Electronics and Mobile Communication Conference, IEMCON 2022, 2022, pp. 546–553. doi: 10.1109/IEMCON56893.2022.9946451.
K. Szalewicz, “The 2010 Benjamin Franklin Medal in Physics presented to J. Ignacio Cirac, David J. Wineland and Peter Zoller,” J. Franklin Inst., vol. 351, no. 1, pp. 51–56, 2014, doi: 10.1016/j.jfranklin.2012.12.005.
S. J. Bickley, H. F. Chan, S. L. Schmidt, and B. Torgler, “Quantum-sapiens: the quantum bases for human expertise, knowledge, and problem-solving,” Technol. Anal. Strateg. Manag., vol. 33, no. 11, pp. 1290–1302, 2021, doi: 10.1080/09537325.2021.1921137.
J. Weinman, Cloudonomics: The Business Value of Cloud Computing (Google eBook). 2012. Accessed: Oct. 04, 2023. [Online]. Available: https://onlinelibrary.wiley.com/doi/pdf/10.1002/9781119204732
E. Rieffel and W. Polak, “An introduction to quantum computing for non-physicists,” ACM Comput. Surv., vol. 32, no. 3, pp. 300–335, 2000, doi: 10.1145/367701.367709.
I. L. Chuang, L. M. K. Vandersypen, X. Zhou, D. W. Leung, and S. Lloyd, “Experimental realization of a quantum algorithm,” Nature, vol. 393, no. 6681, pp. 143–146, 1998, doi: 10.1038/30181.
M. Schuld and F. Petruccione, Supervised Learning with Quantum Computers. 2019. Accessed: Oct. 05, 2023. [Online]. Available: https://link.springer.com/content/pdf/10.1007/978-3-319-96424-9.pdf
S. Kak and H. Hirsh, “Quantum computing and AI A quantum leap for AI,” 1994. Accessed: Oct. 05, 2023. [Online]. Available: https://www.researchgate.net/profile/Abu-Rayhan-11/publication/372722253_Quantum_Computing_and_AI_A_Quantum_Leap_in_Intelligence/links/64c4bac2141074110ee2c469/Quantum-Computing-and-AI-A-Quantum-Leap-in-Intelligence.pdf
G. Dauphinais and D. Poulin, “Fault-Tolerant Quantum Error Correction for non-Abelian Anyons,” Commun. Math. Phys., vol. 355, no. 2, pp. 519–560, Oct. 2017, doi: 10.1007/s00220-017-2923-9.
V. Negnevitsky, “Feedback-stabilised quantum states in a mixed-species ion system,” Dr. Thesis ETH Zurich, vol. 10, no. 25322, 2018, Accessed: Oct. 05, 2023. [Online]. Available: https://www.research-collection.ethz.ch/handle/20.500.11850/295923
S. Johri et al., “Nearest centroid classification on a trapped ion quantum computer,” npj Quantum Inf., vol. 7, no. 1, 2021, doi: 10.1038/s41534-021-00456-5.
A. Pyrkov et al., “Quantum computing for near-term applications in generative chemistry and drug discovery,” Drug Discovery Today, vol. 28, no. 8. 2023. doi: 10.1016/j.drudis.2023.103675.
M. Cerezo, G. Verdon, H. Y. Huang, L. Cincio, and P. J. Coles, “Challenges and opportunities in quantum machine learning,” Nat. Comput. Sci., vol. 2, no. 9, pp. 567–576, 2022, doi: 10.1038/s43588-022-00311-3.
S. Gore, I. Dutt, D. Shyam Prasad, C. Ambhika, A. Sundaram, and D. Nagaraju, “Exploring the Path to Sustainable Growth with Augmented Intelligence by Integrating CSR into Economic Models,” 2023 Second Int. Conf. Augment. Intell. Sustain. Syst., pp. 265–271, Aug. 2023, doi: 10.1109/ICAISS58487.2023.10250636.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.