近期量子自然语言处理基础
Foundations for Near-Term Quantum Natural Language Processing
摘要：
我们为近期的量子自然语言处理（QNLP）提供了概念和数学基础，并以量子计算机科学家友好的方式这样做。我们选择了一个简要的介绍风格，并提供参考支持的经验证据和正式声明有关数学的一般性。我们还记得，我们采用的自然语言的量子模型如何将语言意义与丰富的语言结构（尤其是语法）结合起来。特别是，它需要一个类似量子的模型来结合意义和结构，这一事实将QNLP建立为量子原生的，与量子系统的模拟相提并论。此外，现在领先的噪声中尺度量子（NISQ）范式在量子硬件上编码经典数据，变分量子线路，使NISQ特别QNLP友好：语言结构可以编码为免费午餐，与明显指数昂贵的经典语法编码相反。QNLP任务的量子加速已经在Will Zeng之前的工作中建立。在这里，我们提供了一个更广泛的任务，都享有同样的优势。图解推理是QNLP的核心。首先，量子模型通过范畴量子力学的图解形式将语言解释为量子过程。其次，通过ZX-演算将这些图转化为量子线路。然后，意义的参数化就变成了要学习的回路变量。我们在量子线路中对语言结构的编码也体现了一种建立词义的新方法，这种方法超越了主流人工智能的当前标准，将语言结构置于维特根斯坦意义即语境的核心。
英文摘要：
We provide conceptual and mathematical foundations for near-term quantum natural language processing (QNLP), and do so in quantum computer scientist friendly terms. We opted for an expository presentation style, and provide references for supporting empirical evidence and formal statements concerning mathematical generality. We recall how the quantum model for natural language that we employ canonically combines linguistic meanings with rich linguistic structure, most notably grammar. In particular, the fact that it takes a quantum-like model to combine meaning and structure, establishes QNLP as quantum-native, on par with simulation of quantum systems. Moreover, the now leading Noisy Intermediate-Scale Quantum (NISQ) paradigm for encoding classical data on quantum hardware, variational quantum circuits, makes NISQ exceptionally QNLP-friendly: linguistic structure can be encoded as a free lunch, in contrast to the apparently exponentially expensive classical encoding of grammar. Quantum speed-up for QNLP tasks has already been established in previous work with Will Zeng. Here we provide a broader range of tasks which all enjoy the same advantage. Diagrammatic reasoning is at the heart of QNLP. Firstly, the quantum model interprets language as quantum processes via the diagrammatic formalism of categorical quantum mechanics. Secondly, these diagrams are via ZX-calculus translated into quantum circuits. Parameterisations of meanings then become the circuit variables to be learned. Our encoding of linguistic structure within quantum circuits also embodies a novel approach for establishing word-meanings that goes beyond the current standards in mainstream AI, by placing linguistic structure at the heart of Wittgenstein's meaning-is-context.
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