  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.
