Toward Quantum Machine Translation of Syntactically Distinct Languages
Mina Abbaszade Mariam Zomorodi Vahid Salari Philip Kurian
The present study aims to explore the feasibility of language translation
using quantum natural language processing algorithms on noisy
intermediate-scale quantum (NISQ) devices. Classical methods in natural
language processing (NLP) struggle with handling large-scale computations
required for complex language tasks, but quantum NLP on NISQ devices holds
promise in harnessing quantum parallelism and entanglement to efficiently
process and analyze vast amounts of linguistic data, potentially
revolutionizing NLP applications. Our research endeavors to pave the way for
quantum neural machine translation, which could potentially offer advantages
over classical methods in the future. We employ Shannon entropy to demonstrate
the significant role of some appropriate angles of rotation gates in the
performance of parametrized quantum circuits. In particular, we utilize these
angles (parameters) as a means of communication between quantum circuits of
different languages. To achieve our objective, we adopt the encoder-decoder
model of classical neural networks and implement the translation task using
long short-term memory (LSTM). Our experiments involved 160 samples comprising
English sentences and their Persian translations. We trained the models with
different optimisers implementing stochastic gradient descent (SGD) as primary
and subsequently incorporating two additional optimizers in conjunction with
SGD. Notably, we achieved optimal results-with mean absolute error of 0.03,
mean squared error of 0.002, and 0.016 loss-by training the best model,
consisting of two LSTM layers and using the Adam optimiser. Our small dataset,
though consisting of simple synonymous sentences with word-to-word mappings,
points to the utility of Shannon entropy as a figure of merit in more complex
machine translation models for intricate sentence structures.