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A Practical Overview of Quantum Computing: Is Exascale Possible?

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arxiv 2306.12346 v1 pith:GLKGNK3Z submitted 2023-06-21 cs.DC cs.ET

classification cs.DCcs.ET
keywords quantumwillstilltoolsadvancesalthoughcurrentdevelopment
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Despite numerous advances in the field and a seemingly ever-increasing amount of investment, we are still some years away from seeing a production quantum computer in action. However, it is possible to make some educated guesses about the operational difficulties and challenges that may be encountered in practice. We can be reasonably confident that the early machines will be hybrid, with the quantum devices used in an apparently similar way to current accelerators such as FPGAs or GPUs. Compilers, libraries and the other tools relied upon currently for development of software will have to evolve/be reinvented to support the new technology, and training courses will have to be rethought completely rather than ``just'' updated alongside them. The workloads we are likely to see making best use of these hybrid machines will initially be few, before rapidly increasing in diversity as we saw with the uptake of GPUs and other new technologies in the past. This will again be helped by the increase in the number of supporting libraries and development tools, and by the gradual re-development of existing software, to make use of the new quantum devices. Unfortunately, at present the problem of error correction is still largely unsolved, although there have been many advances. Quantum computation is very sensitive to noise, leading to frequent errors during execution. Quantum calculations, although asymptotically faster than their equivalents in ``traditional'' HPC, still take time, and while the profiling tools and programming approaches will have to change drastically, many of the skills honed in the current HPC industry will not suddenly become obsolete, but continue to be useful in the quantum era.

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Cited by 1 Pith paper

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  1. Opportunities and challenges of quantum computing for climate modelling

    quant-ph 2025-02 unverdicted novelty 4.0 of 10

    This position paper maps quantum algorithms to four climate modeling tasks and concludes that near-term QML parameterizations are promising but computationally prohibitive at scale.

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