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Machine learning in quantum spaces

Author

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  • Maria Schuld

Abstract

Ordinary computers can perform machine learning by comparing mathematical representations of data. An experiment demonstrates how quantum computing could use quantum-mechanical representations instead.

Suggested Citation

  • Maria Schuld, 2019. "Machine learning in quantum spaces," Nature, Nature, vol. 567(7747), pages 179-181, March.
    • Handle: RePEc:nat:nature:v:567:y:2019:i:7747:d:10.1038_d41586-019-00771-0
    DOI: 10.1038/d41586-019-00771-0
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    Citations

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    Cited by:

    1. 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).
    2. Olawale Ayoade & Pablo Rivas & Javier Orduz, 2022. "Artificial Intelligence Computing at the Quantum Level," Data, MDPI, vol. 7(3), pages 1-16, February.
    3. 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.
    4. 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.
    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. 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.
    7. 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.
    8. Domenico Pomarico & Annarita Fanizzi & Nicola Amoroso & Roberto Bellotti & Albino Biafora & Samantha Bove & Vittorio Didonna & Daniele La Forgia & Maria Irene Pastena & Pasquale Tamborra & Alfredo Zit, 2021. "A Proposal of Quantum-Inspired Machine Learning for Medical Purposes: An Application Case," Mathematics, MDPI, vol. 9(4), pages 1-15, February.
    9. Boualem Djehiche & Björn Löfdahl, 2021. "Quantum Support Vector Regression for Disability Insurance," Risks, MDPI, vol. 9(12), pages 1-9, December.

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