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What do you think of > In some of its applications, the original
> Zeng-Coecke algorithm relies on the existence of a quantum random access memory (QRAM) [22],
> which is not yet known to be efficiently implementable in the absence of fault tolerant scalable quantum
> computers [1, 7]. Here we take a different approach, using the classical ansatz parameters to encode the ¨
> distributional embedding and avoiding the need for QRAM entirely. The cost function for the parameter
> optimisation is informed by a corpus, already parsed and POS-tagged by classical means. Source: Quantum Natural Language Processing on Near-Term Quantum Computers https://arxiv.org/abs/2005.04147 Following my intuition, i.e. as an outsider that has been watching the progress of quantum NLP since 2012, I see the current academic situation in quantum computing as in the process of merging two branches, one being the traditional quantum computing field with concerns stemming and application thought in mathematics, computing theory, physics(and upwards chemistry->biochemistry->biology), the other branch being a fork carried out by Coecke (quantum logic), Abramsky (computer science) and Sadrzadeh (epistemic logic) who saw in categorial formalisms of quantum logic a way to mix compositional (syntax, logical rules) and distributional (statistics, "bag-of-neighbor-words") representations of meaning. In this regard they bring new methods but also new applications of quantum computing, with a focus on NLP, as language given this "natural tensor structure [20, 35, 23] [...] can be considered quantum-native [48, 2, 8]." (same paper). |