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Quantum computing in molecular magnets

Author

Listed:
  • Michael N. Leuenberger

    (University of Basel)

  • Daniel Loss

    (University of Basel)

Abstract

Shor and Grover demonstrated that a quantum computer can outperform any classical computer in factoring numbers1 and in searching a database2 by exploiting the parallelism of quantum mechanics. Whereas Shor's algorithm requires both superposition and entanglement of a many-particle system3, the superposition of single-particle quantum states is sufficient for Grover's algorithm4. Recently, the latter has been successfully implemented5 using Rydberg atoms. Here we propose an implementation of Grover's algorithm that uses molecular magnets6,7,8,9,10, which are solid-state systems with a large spin; their spin eigenstates make them natural candidates for single-particle systems. We show theoretically that molecular magnets can be used to build dense and efficient memory devices based on the Grover algorithm. In particular, one single crystal can serve as a storage unit of a dynamic random access memory device. Fast electron spin resonance pulses can be used to decode and read out stored numbers of up to 105, with access times as short as 10-10 seconds. We show that our proposal should be feasible using the molecular magnets Fe8 and Mn12.

Suggested Citation

  • Michael N. Leuenberger & Daniel Loss, 2001. "Quantum computing in molecular magnets," Nature, Nature, vol. 410(6830), pages 789-793, April.
  • Handle: RePEc:nat:nature:v:410:y:2001:i:6830:d:10.1038_35071024
    DOI: 10.1038/35071024
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    Citations

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    Cited by:

    1. Andrea Mattioni & Jakob K. Staab & William J. A. Blackmore & Daniel Reta & Jake Iles-Smith & Ahsan Nazir & Nicholas F. Chilton, 2024. "Vibronic effects on the quantum tunnelling of magnetisation in Kramers single-molecule magnets," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    2. Kashyap, Ravi, 2021. "Artificial Intelligence: A Child’s Play," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    3. Jorge Trasobares & Juan Carlos Martín-Romano & Muhammad Waqas Khaliq & Sandra Ruiz-Gómez & Michael Foerster & Miguel Ángel Niño & Patricia Pedraz & Yannick. J. Dappe & Marina Calero Ory & Julia García, 2023. "Hybrid molecular graphene transistor as an operando and optoelectronic platform," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    4. Dylan Errulat & Katie L. M. Harriman & Diogo A. Gálico & Elvin V. Salerno & Johan Tol & Akseli Mansikkamäki & Mathieu Rouzières & Stephen Hill & Rodolphe Clérac & Muralee Murugesu, 2024. "Slow magnetic relaxation in a europium(II) complex," Nature Communications, Nature, vol. 15(1), pages 1-8, December.
    5. Ravi Kashyap, 2019. "Imitation in the Imitation Game," Papers 1911.06893, arXiv.org.
    6. Chenli Huang & Rong Sun & Lipiao Bao & Xinyue Tian & Changwang Pan & Mengyang Li & Wangqiang Shen & Kun Guo & Bingwu Wang & Xing Lu & Song Gao, 2023. "A hard molecular nanomagnet from confined paramagnetic 3d-4f spins inside a fullerene cage," Nature Communications, Nature, vol. 14(1), pages 1-8, December.
    7. Yu-Xia Wang & Dan Su & Yinina Ma & Young Sun & Peng Cheng, 2023. "Electrical detection and modulation of magnetism in a Dy-based ferroelectric single-molecule magnet," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    8. 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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