Parametrized Quantum Circuits of Synonymous Sentences in Quantum Natural Language Processing
This work addresses the challenge of extending QNLP to non-English languages, specifically Persian, for researchers interested in cross-lingual quantum semantic comparisons.
This paper develops a compositional vector-based semantics for positive transitive sentences in Quantum Natural Language Processing (QNLP) for Persian. It compares the parametrized quantum circuits of synonymous sentences in English and Persian, translating DisCoCat diagrams into quantum circuits via ZX-calculus and a bigraph method.
In this paper, we develop a compositional vector-based semantics of positive transitive sentences in quantum natural language processing for a non-English language, i.e. Persian, to compare the parametrized quantum circuits of two synonymous sentences in two languages, English and Persian. By considering grammar+meaning of a transitive sentence, we translate DisCoCat diagram via ZX-calculus into quantum circuit form. Also, we use a bigraph method to rewrite DisCoCat diagram and turn into quantum circuit in the semantic side.