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Showing 1–4 of 4 results
Advanced filters: Author: Evert van Nieuwenburg Clear advanced filters

  • A neural-network technique can exploit the power of machine learning to mine the exponentially large data sets characterizing the state space of condensed-matter systems. Topological transitions and many-body localization are first on the list.

    • Evert P. L. van Nieuwenburg
    • Ye-Hua Liu
    • Sebastian D. Huber
    Research
    Nature Physics
    Volume: 13, P: 435-439

  • Finding a parameter that can accurately identify the order–disorder phase transition, especially for complex physical systems with high-dimensional configurational space, is a challenging task. Recent work proposes a machine learning approach to effectively tackle this challenge.

    • Evert van Nieuwenburg
    News & Views
    Nature Computational Science
    Volume: 1, P: 644-645

  • Real-time adaptive control of a qubit has been demonstrated but limited to single-axis Hamiltonian estimation. Here the authors implement two-axis control of a singlet-triplet spin qubit with two fluctuating Hamiltonian parameters, resulting in improved quality of coherent oscillations.

    • Fabrizio Berritta
    • Torbjørn Rasmussen
    • Ferdinand Kuemmeth
    ResearchOpen Access
    Nature Communications
    Volume: 15, P: 1-9