# Quantum Natural Language Processing: Challenges and Opportunities
## 7 Discussion
After that, several studies in the literature have demonstrated that quantum speedup can more likely affect NLP tasks related to similarity calculations. This has meant that subsequent work has focused not only on the similarity of the query and the documents (Information Retrieval) but also on the similarity of the question and the answers (question answering) and the similarity of the sentences (text classification). Concerning Question Answering, models that combine QLM and end-to-end neural networks to exploit the density matrix better have been developed (NNLQM). Subsequently, other work has highlighted the inability of early models to apply the fundamental theoretical connections between quantum theory and neural networks in the language modeling process. Therefore, more sophisticated question answering models have been proposed in order to model the complex interactions among ambiguous words with multiple meanings using properties such as the Quantum Many-body Wave Function. Moving to the text classification task, In this case, the QLMs have focused on a specific case, i.e., sentiment analysis task. The interest, as well as the difficulty of this task, lies in the interactions between the utterances and the variability of the datasets. Sentiment datasets can have different annotations based on the emotions/sentiments involved in the analysis. The task has been approached in different ways, using a density matrix-based convolutional network to identify interactions within each utterance or formalizing a tensor network method for natural language representation. Notice that almost all of these models are based on a density matrix defined in a quantum probabilistic space. Density matrix has proven to be an effective way in representing and modeling language in different NLP tasks, encoding more semantic dependencies with respect to classic word embeddings. Moreover, NLP tasks--although well known in the literature--are relatively simple and structured in a comparable manner. Concerning evaluation, although some models claim to outperform deep learning-based approaches, a proper comparative analysis between QLMs is currently not possible. In fact, there is still no agreement on either the baseline dataset or the metrics chosen by QLMs; each model has been tested against different baselines and compared with a different set of other models. The experimental stage of QNLP is even more evident, moving to approaches running on real quantum hardware. Significantly few approaches have been developed so far, and there is a further simplification concerning NLP tasks. The tasks remain roughly the same (question answering [63], text classification [64] and a preliminary experiment on translation of simple sentences [65]; the experiments have been carried out on custom small-scale datasets with simple sentences and a minimal vocabulary. Even the largest dataset used so far in a NISQ experiment (100 sentences for a classification task in [64]) remains on dimension enormously smaller if compared to that of deep learning-based architectures. Notice that at the time of the listed work, NISQ devices can have 50-100 qubits. Currently, the most powerful IBM Quantum machines reach 127 qubits (source: [[https://www.ibm.com/quantum-computing/systems/](https://www.ibm.com/quantum-computing/systems/)]([https://www.ibm.com/quantum-computing/systems/](https://www.ibm.com/quantum-computing/systems/)) accessed on on 1 June 2022). Although these limitations make difficult to compare quantum-hardware approaches with classical deep-learning ones, as hardware power grows, this drawback may be removed. It is highly feasible that, in the future, NISQ devices will be able to work on the benchmark datasets well known in NLP community. Notice that hardware limits of QNLP are the same shared with the whole field of quantum computing (i.e., unrealized QRAM, the limited number of qubits, lack of fault-tolerant quantum machine).