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Experimental realization of Shor's quantum factoring algorithm using nuclear magnetic resonance

Author

Listed:
  • Lieven M. K. Vandersypen

    (IBM Almaden Research Center
    Solid State and Photonics Laboratory, Stanford University)

  • Matthias Steffen

    (IBM Almaden Research Center
    Solid State and Photonics Laboratory, Stanford University)

  • Gregory Breyta

    (IBM Almaden Research Center)

  • Costantino S. Yannoni

    (IBM Almaden Research Center)

  • Mark H. Sherwood

    (IBM Almaden Research Center)

  • Isaac L. Chuang

    (IBM Almaden Research Center
    Solid State and Photonics Laboratory, Stanford University)

Abstract

The number of steps any classical computer requires in order to find the prime factors of an l-digit integer N increases exponentially with l, at least using algorithms known at present1. Factoring large integers is therefore conjectured to be intractable classically, an observation underlying the security of widely used cryptographic codes1,2. Quantum computers3, however, could factor integers in only polynomial time, using Shor's quantum factoring algorithm4,5,6. Although important for the study of quantum computers7, experimental demonstration of this algorithm has proved elusive8,9,10. Here we report an implementation of the simplest instance of Shor's algorithm: factorization of N = 15 (whose prime factors are 3 and 5). We use seven spin-1/2 nuclei in a molecule as quantum bits11,12, which can be manipulated with room temperature liquid-state nuclear magnetic resonance techniques. This method of using nuclei to store quantum information is in principle scalable to systems containing many quantum bits13, but such scalability is not implied by the present work. The significance of our work lies in the demonstration of experimental and theoretical techniques for precise control and modelling of complex quantum computers. In particular, we present a simple, parameter-free but predictive model of decoherence effects14 in our system.

Suggested Citation

  • 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.
  • Handle: RePEc:nat:nature:v:414:y:2001:i:6866:d:10.1038_414883a
    DOI: 10.1038/414883a
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    Cited by:

    1. 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, June.
    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. Dennis Willsch & Madita Willsch & Fengping Jin & Hans De Raedt & Kristel Michielsen, 2023. "Large-Scale Simulation of Shor’s Quantum Factoring Algorithm," Mathematics, MDPI, vol. 11(19), pages 1-38, October.
    4. Hu, Jie-Ru & Zhang, Zuo-Yuan & Liu, Jin-Ming, 2024. "Implementation of three-qubit Deutsch-Jozsa algorithm with pendular states of polar molecules by optimal control," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 635(C).
    5. Cristina Andriani & Lorenzo Bencivelli & Antonio Castellucci & Mauro De Santis & Sabina Marchetti & Giovanna Piantanida, 2024. "The quantum challenge: implications and strategies for a secure financial system," Questioni di Economia e Finanza (Occasional Papers) 877, Bank of Italy, Economic Research and International Relations Area.
    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.

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