[4][5][6] The first quantum algorithm for natural language processing used the DisCoCat framework and Grover's algorithm to show a quadratic quantum speedup for a text classification task.
Thus, they are not applicable to the noisy intermediate-scale quantum (NISQ) computers available today.
The algorithm of Zeng and Coecke[1] was adapted to the constraints of NISQ computers and implemented on IBM quantum computers to solve binary classification tasks.
[8][9] Instead of loading classical word vectors onto a quantum memory, the word vectors are computed directly as the parameters of quantum circuits.
These parameters are optimised using methods from quantum machine learning to solve data-driven tasks such as question answering,[8] machine translation[10] and even algorithmic music composition.