We introduce an open-access web tool that estimates when quantum systems will outperform classical computers for specific algorithms on a cost‑equivalent basis. Built on the Choi–Moses–Thompson framework, it lets users vary assumptions about error correction, gate speeds, overheads, connectivity, hardware roadmaps, and classical baselines. We find that expected timing for advantage is robust for some algorithms (e.g., Shor's) but contingent for others (e.g., Grover's), depending on technical factors.

Deep learning's recent history has been one of achievement: from triumphing over humans in the game of Go to world-leading performance in image classification, voice recognition, translation, and other tasks. But this progress has come with a voracious appetite for computing power. This article catalogs the extent of this dependency, showing that progress across a wide variety of applications is strongly reliant on increases in computing power. Extrapolating forward this reliance reveals that progress along current lines is rapidly becoming economically, technically, and environmentally unsustainable. Thus, continued progress in these applications will require dramatically more computationally-efficient methods, which will either have to come from changes to deep learning or from moving to other machine learning methods.

Algorithms are one of the fundamental building blocks of computing. But current evidence about how fast algorithms improve is anecdotal, using small numbers of case studies to extrapolate. In this work, we gather data from 57 textbooks and more than 1,137 research papers to present the first systematic view of algorithm progress ever assembled. There is enormous variation. Around half of all algorithm families experience little or no improvement. At the other extreme, 13% experience transformative improvements, radically changing how and where they can be used. Overall, we find that, for moderate-sized problems, 30% to 45% of algorithmic families had improvements comparable or greater than those that users experienced from Moore's Law and other hardware advances