• [LINK] Disentangling Hype from Practicality: On Realistically Achieving

    From Computer Nerd Kev@21:1/5 to All on Sat May 20 13:59:38 2023
    Disentangling Hype from Practicality: On Realistically Achieving
    Quantum Advantage
    By Torsten Hoefler, Thomas Haner, Matthias Troyer, May 2023
    - https://cacm.acm.org/magazines/2023/5/272276-disentangling-hype-from-practicality-on-realistically-achieving-quantum-advantage/fulltext

    "Operating on fundamentally different principles than conventional
    computers, quantum computers promise to solve a variety of
    important problems that seemed forever intractable on classical
    computers. Leveraging the quantum foundations of nature, the time
    to solve certain problems on quantum computers grows more slowly
    with the size of the problem than on classical computers-this is
    called quantum speedup. Going beyond quantum supremacy,2 which was
    the demonstration of a quantum computer outperforming a classical
    one for an artificial problem, an important question is finding
    meaningful applications (of academic or commercial interest) that
    can realistically be solved faster on a quantum computer than on a
    classical one. We call this a practical quantum advantage, or
    quantum practicality for short.

    There is a maze of hard problems that have been suggested to profit
    from quantum acceleration: from cryptanalysis, chemistry and
    materials science, to optimization, big data, machine learning,
    database search, drug design and protein folding, fluid dynamics
    and weather prediction. But which of these applications
    realistically offer a potential quantum advantage in practice? For
    this, we cannot only rely on asymptotic speedups but must consider
    the constants involved. Being optimistic in our outlook for quantum
    computers, we identify clear guidelines for quantum practicality
    and use them to classify which of the many proposed applications
    for quantum computing show promise and which ones would require
    significant algorithmic improvements to become practical and
    relevant." ...

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