IDEAS home Printed from https://ideas.repec.org/a/gam/jdataj/v7y2022i3p28-d759196.html
   My bibliography  Save this article

Artificial Intelligence Computing at the Quantum Level

Author

Listed:
  • Olawale Ayoade

    (Department of Physics, Baylor University, One Bear Place #97316, Waco, TX 76706, USA)

  • Pablo Rivas

    (Department of Computer Science, Baylor University, One Bear Place #97141, Waco, TX 76712, USA)

  • Javier Orduz

    (Department of Computer Science, Baylor University, One Bear Place #97141, Waco, TX 76712, USA)

Abstract

The extraordinary advance in quantum computation leads us to believe that, in the not-too-distant future, quantum systems will surpass classical systems. Moreover, the field’s rapid growth has resulted in the development of many critical tools, including programmable machines (quantum computers) that execute quantum algorithms and the burgeoning field of quantum machine learning, which investigates the possibility of faster computation than traditional machine learning. In this paper, we provide a thorough examination of quantum computing from the perspective of a physicist. The purpose is to give laypeople and scientists a broad but in-depth understanding of the area. We also recommend charts that summarize the field’s diversions to put the whole field into context.

Suggested Citation

  • Olawale Ayoade & Pablo Rivas & Javier Orduz, 2022. "Artificial Intelligence Computing at the Quantum Level," Data, MDPI, vol. 7(3), pages 1-16, February.
  • Handle: RePEc:gam:jdataj:v:7:y:2022:i:3:p:28-:d:759196
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2306-5729/7/3/28/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2306-5729/7/3/28/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Maria Schuld, 2019. "Machine learning in quantum spaces," Nature, Nature, vol. 567(7747), pages 179-181, March.
    2. Lieven M. K. Vandersypen & Matthias Steffen & Gregory Breyta & Costantino S. Yannoni & Mark H. Sherwood & Isaac L. Chuang, 2001. "Experimental realization of Shor's quantum factoring algorithm using nuclear magnetic resonance," Nature, Nature, vol. 414(6866), pages 883-887, December.
    3. Philip Ball, 2021. "First quantum computer to pack 100 qubits enters crowded race," Nature, Nature, vol. 599(7886), pages 542-542, November.
    4. Jacob Biamonte & Peter Wittek & Nicola Pancotti & Patrick Rebentrost & Nathan Wiebe & Seth Lloyd, 2017. "Quantum machine learning," Nature, Nature, vol. 549(7671), pages 195-202, September.
    5. K. Kim & M.-S. Chang & S. Korenblit & R. Islam & E. E. Edwards & J. K. Freericks & G.-D. Lin & L.-M. Duan & C. Monroe, 2010. "Quantum simulation of frustrated Ising spins with trapped ions," Nature, Nature, vol. 465(7298), pages 590-593, June.
    6. Cafaro, Carlo, 2017. "Geometric algebra and information geometry for quantum computational software," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 470(C), pages 154-196.
    7. Alexander McCaskey & Eugene Dumitrescu & Mengsu Chen & Dmitry Lyakh & Travis Humble, 2018. "Validating quantum-classical programming models with tensor network simulations," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-19, December.
    8. Vojtěch Havlíček & Antonio D. Córcoles & Kristan Temme & Aram W. Harrow & Abhinav Kandala & Jerry M. Chow & Jay M. Gambetta, 2019. "Supervised learning with quantum-enhanced feature spaces," Nature, Nature, vol. 567(7747), pages 209-212, March.
    9. Julio T. Barreiro & Markus Müller & Philipp Schindler & Daniel Nigg & Thomas Monz & Michael Chwalla & Markus Hennrich & Christian F. Roos & Peter Zoller & Rainer Blatt, 2011. "An open-system quantum simulator with trapped ions," Nature, Nature, vol. 470(7335), pages 486-491, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wei-Ming Li & Shi-Ju Ran, 2022. "Non-Parametric Semi-Supervised Learning in Many-Body Hilbert Space with Rescaled Logarithmic Fidelity," Mathematics, MDPI, vol. 10(6), pages 1-15, March.
    2. Ajagekar, Akshay & You, Fengqi, 2022. "Quantum computing and quantum artificial intelligence for renewable and sustainable energy: A emerging prospect towards climate neutrality," Renewable and Sustainable Energy Reviews, Elsevier, vol. 165(C).
    3. Daniel J. Egger & Claudio Gambella & Jakub Marecek & Scott McFaddin & Martin Mevissen & Rudy Raymond & Andrea Simonetto & Stefan Woerner & Elena Yndurain, 2020. "Quantum Computing for Finance: State of the Art and Future Prospects," Papers 2006.14510, arXiv.org, revised Jan 2021.
    4. Vicente Moret-Bonillo & Samuel Magaz-Romero & Eduardo Mosqueira-Rey, 2022. "Quantum Computing for Dealing with Inaccurate Knowledge Related to the Certainty Factors Model," Mathematics, MDPI, vol. 10(2), pages 1-21, January.
    5. Sofiene Jerbi & Lukas J. Fiderer & Hendrik Poulsen Nautrup & Jonas M. Kübler & Hans J. Briegel & Vedran Dunjko, 2023. "Quantum machine learning beyond kernel methods," Nature Communications, Nature, vol. 14(1), pages 1-8, December.
    6. Jonas Jäger & Roman V. Krems, 2023. "Universal expressiveness of variational quantum classifiers and quantum kernels for support vector machines," Nature Communications, Nature, vol. 14(1), pages 1-7, December.
    7. Johannes Herrmann & Sergi Masot Llima & Ants Remm & Petr Zapletal & Nathan A. McMahon & Colin Scarato & François Swiadek & Christian Kraglund Andersen & Christoph Hellings & Sebastian Krinner & Nathan, 2022. "Realizing quantum convolutional neural networks on a superconducting quantum processor to recognize quantum phases," Nature Communications, Nature, vol. 13(1), pages 1-7, December.
    8. Wu, Jiang & Ou, Guiyan & Liu, Xiaohui & Dong, Ke, 2022. "How does academic education background affect top researchers’ performance? Evidence from the field of artificial intelligence," Journal of Informetrics, Elsevier, vol. 16(2).
    9. Ferenc Iglói & Cécile Monthus, 2018. "Strong disorder RG approach – a short review of recent developments," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 91(11), pages 1-25, November.
    10. Ajagekar, Akshay & You, Fengqi, 2021. "Quantum computing based hybrid deep learning for fault diagnosis in electrical power systems," Applied Energy, Elsevier, vol. 303(C).
    11. Jurgita Bruneckiene & Robertas Jucevicius & Ineta Zykiene & Jonas Rapsikevicius & Mantas Lukauskas, 2019. "Assessment of Investment Attractiveness in European Countries by Artificial Neural Networks: What Competences are Needed to Make a Decision on Collective Well-Being?," Sustainability, MDPI, vol. 11(24), pages 1-23, December.
    12. Ad. J. W. van de Gevel & Charles N. Noussair, 2013. "The Nexus between Artificial Intelligence and Economics," SpringerBriefs in Economics, Springer, edition 127, number 978-3-642-33648-5, September.
    13. Boualem Djehiche & Björn Löfdahl, 2021. "Quantum Support Vector Regression for Disability Insurance," Risks, MDPI, vol. 9(12), pages 1-9, December.
    14. Li, Nianqiao & Yan, Fei & Hirota, Kaoru, 2022. "Quantum data visualization: A quantum computing framework for enhancing visual analysis of data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 599(C).
    15. Fode Zhang & Hon Keung Tony Ng & Yimin Shi & Ruibing Wang, 2019. "Amari–Chentsov structure on the statistical manifold of models for accelerated life tests," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(1), pages 77-105, March.
    16. Midya Parto & Christian Leefmans & James Williams & Franco Nori & Alireza Marandi, 2023. "Non-Abelian effects in dissipative photonic topological lattices," Nature Communications, Nature, vol. 14(1), pages 1-8, December.
    17. Elies Gil-Fuster & Jens Eisert & Carlos Bravo-Prieto, 2024. "Understanding quantum machine learning also requires rethinking generalization," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    18. Guo, Mingchao & Liu, Hailing & Li, Yongmei & Li, Wenmin & Gao, Fei & Qin, Sujuan & Wen, Qiaoyan, 2022. "Quantum algorithms for anomaly detection using amplitude estimation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    19. Maria Mannone & Antonio Chella & Giovanni Pilato & Valeria Seidita & Filippo Vella & Salvatore Gaglio, 2024. "Modeling Robotic Thinking and Creativity: A Classic–Quantum Dialogue," Mathematics, MDPI, vol. 12(5), pages 1-16, February.
    20. Gong, Li-Hua & Xiang, Ling-Zhi & Liu, Si-Hang & Zhou, Nan-Run, 2022. "Born machine model based on matrix product state quantum circuit," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jdataj:v:7:y:2022:i:3:p:28-:d:759196. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.