IDEAS home Printed from https://ideas.repec.org/a/eee/telpol/v44y2020i6s030859612030001x.html

Quantum Artificial Intelligence: A “precautionary” U.S. approach?

Author

Listed:
  • Taylor, Richard D.

Abstract

Quantum Computing (QC) and Quantum Artificial Intelligence (QAI) are two powerful new technologies whose potential impacts are just starting to be appreciated. As important as they are likely to be, their implications are still little known. This article's purpose is an attempt to provide a policy space within which to begin fill that void.

Suggested Citation

  • Taylor, Richard D., 2020. "Quantum Artificial Intelligence: A “precautionary” U.S. approach?," Telecommunications Policy, Elsevier, vol. 44(6).
  • Handle: RePEc:eee:telpol:v:44:y:2020:i:6:s030859612030001x
    DOI: 10.1016/j.telpol.2020.101909
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S030859612030001X
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.telpol.2020.101909?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. Taylor, Richard D., 2017. "The next stage of U.S. communications policy: The emerging embedded infosphere," Telecommunications Policy, Elsevier, vol. 41(10), pages 1039-1055.
    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. Deepak Ranga & Aryan Rana & Sunil Prajapat & Pankaj Kumar & Kranti Kumar & Athanasios V. Vasilakos, 2024. "Quantum Machine Learning: Exploring the Role of Data Encoding Techniques, Challenges, and Future Directions," Mathematics, MDPI, vol. 12(21), pages 1-32, October.
    2. Abraham Itzhak Weinberg, 2025. "Hybrid Quantum-Classical Ensemble Learning for S\&P 500 Directional Prediction," Papers 2512.15738, arXiv.org.
    3. Liyun Su & Dan Li & Dongyang Qiu, 2025. "BLS-QLSTM: a novel hybrid quantum neural network for stock index forecasting," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 12(1), pages 1-15, December.
    4. Anita Gurumurthy & Nandini Chami, 2021. "Towards a Global Digital Constitutionalism: A Radical New Agenda for UN75," Development, Palgrave Macmillan;Society for International Deveopment, vol. 64(1), pages 29-38, June.
    5. 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.
    6. Bikram Khanal & Pablo Rivas, 2024. "A Modified Depolarization Approach for Efficient Quantum Machine Learning," Mathematics, MDPI, vol. 12(9), pages 1-17, May.
    7. Taofeek Adeshina Yusuff & Kenechukwu Francis Iloeje & Sylviastella Favour Peteranaba & Victoria Sharon Akinlolu & Nimotalai Olusola Kassim & Zuraifa Hamidu, 2025. "Creating Quantum-Powered Epidemiological Models Enabling Proactive Responses to Pandemics and Emerging Health Threats," International Journal of Scientific Research and Modern Technology, Prasu Publications, vol. 4(10), pages 39-58.
    8. Fujii, Hidemichi & Managi, Shunsuke, 2018. "Trends and priority shifts in artificial intelligence technology invention: A global patent analysis," Economic Analysis and Policy, Elsevier, vol. 58(C), pages 60-69.
    9. Rana Muhammad Adnan & Abolfazl Jaafari & Aadhityaa Mohanavelu & Ozgur Kisi & Ahmed Elbeltagi, 2021. "Novel Ensemble Forecasting of Streamflow Using Locally Weighted Learning Algorithm," Sustainability, MDPI, vol. 13(11), pages 1-19, May.
    10. Amine Zeguendry & Zahi Jarir & Mohamed Quafafou, 2026. "Hybrid Ensemble and Quantum Machine Learning Framework for River Water Quality Prediction and Classification," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 40(2), pages 1-20, January.
    11. Hui Gong & Akash Sedai & Thomas Schroeder & Francesca Medda, 2026. "Quantum Computing for Financial Transformation: A Review of Optimisation, Pricing, Risk, Machine Learning, and Post-Quantum Security," Papers 2604.08180, arXiv.org.
    12. Yen-Jui Chang & Wei-Ting Wang & Hao-Yuan Chen & Shih-Wei Liao & Ching-Ray Chang, 2023. "A novel approach for quantum financial simulation and quantum state preparation," Papers 2308.01844, arXiv.org, revised Apr 2024.
    13. Dylan Herman & Cody Googin & Xiaoyuan Liu & Alexey Galda & Ilya Safro & Yue Sun & Marco Pistoia & Yuri Alexeev, 2022. "A Survey of Quantum Computing for Finance," Papers 2201.02773, arXiv.org, revised Jun 2022.
    14. Olawale Ayoade & Pablo Rivas & Javier Orduz, 2022. "Artificial Intelligence Computing at the Quantum Level," Data, MDPI, vol. 7(3), pages 1-16, February.
    15. 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.
    16. Syed Muhammad Abuzar Rizvi & Usama Inam Paracha & Uman Khalid & Kyesan Lee & Hyundong Shin, 2025. "Quantum Machine Learning: Towards Hybrid Quantum-Classical Vision Models," Mathematics, MDPI, vol. 13(16), pages 1-14, August.
    17. Victor Oliveira Santos & Felipe Pinto Marinho & Paulo Alexandre Costa Rocha & Jesse Van Griensven Thé & Bahram Gharabaghi, 2024. "Application of Quantum Neural Network for Solar Irradiance Forecasting: A Case Study Using the Folsom Dataset, California," Energies, MDPI, vol. 17(14), pages 1-26, July.
    18. Zitong Li & Tailong Xiao & Xiaoyang Deng & Guihua Zeng & Weimin Li, 2024. "Optimizing Variational Quantum Neural Networks Based on Collective Intelligence," Mathematics, MDPI, vol. 12(11), pages 1-14, May.
    19. 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).
    20. 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.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:eee:telpol:v:44:y:2020:i:6:s030859612030001x. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/30471/description#description .

    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.