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Disentangling Hype from Practicality: On Realistically Achieving Quantum Advantage

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arxiv 2307.00523 v1 pith:WHX4XQ4R submitted 2023-07-02 quant-ph cs.DScs.PFphysics.pop-ph

classification quant-phcs.DScs.PFphysics.pop-ph
keywords quantumapplicationsalgorithmscomputingadvantageclassicalcomputersguidelines
verification ladder T0 review T1 audit T2 compute T3 formal
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Quantum computers offer a new paradigm of computing with the potential to vastly outperform any imagineable classical computer. This has caused a gold rush towards new quantum algorithms and hardware. In light of the growing expectations and hype surrounding quantum computing we ask the question which are the promising applications to realize quantum advantage. We argue that small data problems and quantum algorithms with super-quadratic speedups are essential to make quantum computers useful in practice. With these guidelines one can separate promising applications for quantum computing from those where classical solutions should be pursued. While most of the proposed quantum algorithms and applications do not achieve the necessary speedups to be considered practical, we already see a huge potential in material science and chemistry. We expect further applications to be developed based on our guidelines.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Fractional Quantum Hall Anyons via the Algebraic Topology of Exotic Flux Quanta

    cond-mat.mes-hall 2025-05 conditional novelty 6.0 of 10

    Fractional quantum Hall anyons are re-derived from a non-Lagrangian flux quantization in 2-Cohomotopy, with new predictions for torus degeneracy and defect anyons.

  2. Reducing the sampling complexity of energy estimation in quantum many-body systems using empirical variance information

    quant-ph 2025-02 reject novelty 6.0 of 10

    An adaptive estimator based on empirical Bernstein stopping reduces the number of measurements needed to estimate ground-state energies with rigorous error bounds, by up to an order of magnitude in numerical benchmarks.

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