• Scientists observe quantum speed-up in o

    From ScienceDaily@1:317/3 to All on Thu May 5 22:30:40 2022
    Scientists observe quantum speed-up in optimization problems

    Date:
    May 5, 2022
    Source:
    Harvard University
    Summary:
    Scientists have demonstrated a breakthrough application of
    neutral-atom quantum processors to solve problems of practical use.



    FULL STORY ==========================================================================
    A collaboration between Harvard University with scientists at QuEra
    Computing, MIT, University of Innsbruck and other institutions has
    demonstrated a breakthrough application of neutral-atom quantum processors
    to solve problems of practical use.


    ==========================================================================
    The study was co-led by Mikhail Lukin, the George Vasmer Leverett
    Professor of Physics at Harvard and co-director of the Harvard Quantum Initiative, Markus Greiner, George Vasmer Leverett Professor of Physics,
    and Vladan Vuletic, Lester Wolfe Professor of Physics at MIT. Titled
    "Quantum Optimization of Maximum Independent Set using Rydberg Atom
    Arrays," was published on May 5th, 2022, in Science Magazine.

    Previously, neutral-atom quantum processors had been proposed to
    efficiently encode certain hard combinatorial optimization problems. In
    this landmark publication, the authors not only deploy the first
    implementation of efficient quantum optimization on a real quantum
    computer, but also showcase unprecedented quantum hardware power.

    The calculations were performed on Harvard's quantum processor of 289
    qubits operating in the analog mode, with effective circuit depths up
    to 32. Unlike in previous examples of quantum optimization, the large
    system size and circuit depth used in this work made it impossible to
    use classical simulations to pre- optimize the control parameters. A quantum-classical hybrid algorithm had to be deployed in a closed loop,
    with direct, automated feedback to the quantum processor.

    This combination of system size, circuit depth, and outstanding
    quantum control culminated in a quantum leap: problem instances were
    found with empirically better-than-expected performance on the quantum processor versus classical heuristics. Characterizing the difficulty
    of the optimization problem instances with a "hardness parameter,"
    the team identified cases that challenged classical computers, but
    that were more efficiently solved with the neutral- atom quantum
    processor. A super-linear quantum speed-up was found compared to a
    class of generic classical algorithms. QuEra's open-source packages GenericTensorNetworks.jl and Bloqade.jl were instrumental in discovering
    hard instances and understanding quantum performance.

    "A deep understanding of the underlying physics of the quantum algorithm
    as well as the fundamental limitations of its classical counterpart
    allowed us to realize ways for the quantum machine to achieve a
    speedup," says Madelyn Cain, Harvard graduate student and one of the
    lead authors. The importance of match- making between problem and quantum hardware is central to this work: "In the near future, to extract as much quantum power as possible, it is critical to identify problems that can
    be natively mapped to the specific quantum architecture, with little to
    no overhead," said Shengtao Wang, Senior Scientist at QuEra Computing and
    one of the coinventors of the quantum algorithms used in this work, "and
    we achieved exactly that in this demonstration." The "maximum independent
    set" problem, solved by the team, is a paradigmatic hard task in computer science and has broad applications in logistics, network design, finance,
    and more. The identification of classically challenging problem instances
    with quantum-accelerated solutions paves the path for applying quantum computing to cater to real-world industrial and social needs.

    "These results represent the first step towards bringing useful quantum advantage to hard optimization problems relevant to multiple industries.," added Alex Keesling CEO of QuEra Computing and co-author on the published
    work.

    "We are very happy to see quantum computing start to reach the necessary
    level of maturity where the hardware can inform the development of
    algorithms beyond what can be predicted in advance with classical compute methods. Moreover, the presence of a quantum speedup for hard problem
    instances is extremely encouraging. These results help us develop better algorithms and more advanced hardware to tackle some of the hardest,
    most relevant computational problems."

    ========================================================================== Story Source: Materials provided by Harvard_University. Note: Content
    may be edited for style and length.


    ========================================================================== Journal Reference:
    1. S. Ebadi, A. Keesling, M. Cain, T. T. Wang, H. Levine, D. Bluvstein,
    G.

    Semeghini, A. Omran, J.-G. Liu, R. Samajdar, X.-Z. Luo,
    B. Nash, X. Gao, B. Barak, E. Farhi, S. Sachdev, N. Gemelke,
    L. Zhou, S. Choi, H. Pichler, S.-T. Wang, M. Greiner, V. Vuletic,
    M. D. Lukin. Quantum optimization of maximum independent set using
    Rydberg atom arrays. Science, 2022; DOI: 10.1126/science.abo6587 ==========================================================================

    Link to news story: https://www.sciencedaily.com/releases/2022/05/220505150340.htm

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