Quantum Natural Language Processing (QNLP) is taking huge leaps in solving
the shortcomings of classical Natural Language Processing (NLP) techniques and
moving towards a more "Explainable" NLP system. The current literature around
QNLP focuses primarily on implementing QNLP techniques in sentences in the
English language. In this paper, we propose to enable the QNLP approach to
HINDI, which is the third most spoken language in South Asia. We present the
process of building the parameterized quantum circuits required to undertake
QNLP on Hindi sentences. We use the pregroup representation of Hindi and the
DisCoCat framework to draw sentence diagrams. Later, we translate these
diagrams to Parameterised Quantum Circuits based on Instantaneous Quantum
Polynomial (IQP) style ansatz. Using these parameterized quantum circuits
allows one to train grammar and topic-aware sentence classifiers for the Hindi
Language.

使用 Hindi 的参数化量子电路，通过 Quantum Natural Language Processing (QNLP) 的方法，构建了适用于 Hindi 句子的语法和主题感知的分类器。

实现印地语量子自然语言处理

Enabling Quantum Natural Language Processing for Hindi Language

The DisCoCat model of natural language meaning assigns meaning to a sentence
given: (i) the meanings of its words, and, (ii) its grammatical structure. The
recently introduced DisCoCirc model extends this to text consisting of multiple
sentences. While in DisCoCat all meanings are fixed, in DisCoCirc each sentence
updates meanings of words. In this paper we explore different update mechanisms
for DisCoCirc, in the case where meaning is encoded in density matrices---which
come with several advantages as compared to vectors.
Our starting point are two non-commutative update mechanisms, borrowing one
from quantum foundations research, from Leifer and Spekkens. Unfortunately,
neither of these satisfies any desirable algebraic properties, nor are internal
to the meaning category. By passing to double density matrices we do get an
elegant internal diagrammatic update mechanism.
We also show that (commutative) spiders can be cast as an instance of the
Leifer-Spekkens update mechanism. This result is of interest to quantum
foundations, as it bridges the work in Categorical Quantum Mechanics (CQM) with
that on conditional quantum states. Our work also underpins implementation of
text-level natural language processing on quantum hardware (a.k.a. QNLP), for
which exponential space-gain and quadratic speed-up have previously been
identified.

本研究探讨了 DisCoCirc 模型的不同更新机制以及双重密度矩阵对语义编码的优势，同时将（可交换的）spiders 解释为 Leifer-Spekkens 更新机制的实例。这为在量子硬件上实施文本级自然语言处理提供了基础，先前已确定其具有指数空间增益和二次速度提升的优点。