HackerPapers

arXiv:2307.00523quant-ph

Disentangling Hype from Practicality: On Realistically Achieving Quantum Advantage

Torsten Hoefler, Thomas Haener, Matthias Troyer

Abstract

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.

arXiv comments
Journal reference
CACM May 2023
DOI
Subjects
quant-phQuantum Physics cs.DSData Structures and Algorithms cs.PFPerformance physics.pop-phPopular Physics

On Hacker News

Linked by 2 identifiable commenters in 2 comments across 1 thread, from Jul 2023 to Jul 2023. Found in 1 HN submission from Jul 2023 to Jul 2023. Open submission discussions.

Comments linking this paper, per year.

Discussed in comments1 thread

  1. Quantinuum H-Series quantum computer 2 comments

    • Disentangling hype from practicality: https://arxiv.org/abs/2307.00523

      konradhaView on HNParent context

    • In principle, the advantages of quantum computers are delivered not in speed or throughput, but in algorithmic scaling. For example, the well-known Grover's search algorithm scales as O(sqrt(N)), as opposed to O(n) for classical linear search. Similarly, Shor's factoring…

      fastneutronView on HNParent context

Submitted to Hacker News

Connected through shared HN threads, not inferred similarity or a recommendation.