Entangled Meanings: Classification and Ambiguity Resolution in Near-Term QNLP

We demonstrate two tasks in Quantum Natural Language Processing (QNLP): classification and disambiguation. For the classification task, we utilized an amplitude encoding algorithm and achieved perfect accuracy on the standard lambeq dataset, which is commonly used for benchmarking in quantum NLP. Additionally, we obtained accuracy from 55% to 72.5 % on a more complex and realistic Amazon review dataset, which is still a reasonable result, given the current capability of quantum computing and QNLP. Additionally, we found that UMAP is the best dimension reduction method in our experiment setting. All classification results were done on the default. qubit simulator in pennylane 0.36 python library. Our classification results highlight the potential of quantum algorithms in practical applications. For the disambiguation task, we selected 18 ambiguous nouns, 32 unambiguous nouns, and 18 different verbs. Our experiments using the QASM simulator within the qiskit Python library demonstrated that the simulator could perfectly differentiate between the various meanings of ambiguous nouns in different contexts, achieving perfect differentiation. Furthermore, we extended our study to a real quantum device, the ibm_kyoto quantum computer. There, we tested our disambiguation approach on a subset of 4 random nouns (two ambiguous and two unambiguous) and observed that ibm_kyoto could achieve an accuracy range of 82.1 % to 98.9% in disambiguation tasks, extending the datasets and improving the results of existing ambiguity resolution experiments [1]. Our work demonstrates the capability of quantum computing in dealing with real-world NLP tasks, hence contributing to the advancement of both QML and NLP.