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Heterotic Computing

机译:异质计算

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摘要

Non-classical computation has tended to consider only single computational models: neural, analog, quantum, etc. However, combined computational models can both have more computational power, and more natural programming approaches, than such 'pure' models alone. Here we outline a proposed new approach, which we term heterotic computing. We discuss how this might be incorporated in an accessible refinement-based computational framework for combining diverse computational models, and describe a range of physical exemplars (combinations of classical discrete, quantum discrete, classical analog, and quantum analog) that could be used to demonstrate the capability.
机译:非经典计算往往只考虑单个计算模型:神经模型,模拟模型,量子模型等。但是,与单独的这种“纯”模型相比,组合的计算模型可以具有更大的计算能力和更自然的编程方法。在这里,我们概述了一种提出的新方法,我们将其称为异质计算。我们讨论了如何将其合并到可访问的基于改进的计算框架中以组合各种计算模型,并描述了一系列物理示例(经典离散,量子离散,经典模拟和量子模拟的组合)能力。

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