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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
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    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. Maria Schuld, 2019. "Machine learning in quantum spaces," Nature, Nature, vol. 567(7747), pages 179-181, March.
    6. Philip Ball, 2021. "First quantum computer to pack 100 qubits enters crowded race," Nature, Nature, vol. 599(7886), pages 542-542, November.
    7. 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.
    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.
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