# Quantum Natural Language Processing: Challenges and Opportunities
## 5 Quantum-Inspired Approaches
### Information Retrieval
Since a real comparison is not always possible, only the works that have really compared with benchmark datasets already known in the literature are shown. For each model proposed, the best score obtained for the metric used with respect to the specific dataset is shown in the last column. In brackets, the best score obtained by the baseline with which each approach has been compared is indicated. evaluated only on a set of custom-created document collections and not on any benchmark dataset, making comparison with other models difficult. Inspired by these works, another QLM that incorporated a query expansion framework to overcome restrictions due to limited vocabulary has been proposed in [55]. Despite successful applications of QLMs in the field of Information Retrieval, all these approaches are biased by the fact that the training algorithm for the original QLM is not globally convergent. Then, in order to try to reduce these imperfections affecting the final ranking, approaches that use a QLM are based on a minimal vocabulary that typically coincides with query terms. This compromised results in a significant reduction in the performance capabilities possible with QLM. Therefore, incorporating a query expansion framework into QLM opens up the possibility of using a higher number of terms into modeling. The approach is structured as follows: first, an advanced training algorithm is used to generate first-round ranking, then--in order to identify the expansion terms--a single QLM is built for the top returned results. Finally, the final ranking result is generated by applying a QLM with the new training algorithm to the expanded query. The proposed Quantum Language Model-based Query Expansion (QLM-QE) framework has been tested on TREC datasets showing an improvement when compared to other traditional approaches. In particular, QLM-QE has been compared with four other models to evaluate performance: the Unigram baseline model, RM-HS [80], a query expansion approach inspired by quantum interference, the original QLM proposed by [53], and QMT [81], which is a quantum-like session search model inspired by the Two-State Vector Formalism (TSVF). Performance has been evaluated on TREC datasets, since they are the de facto standard for the performance evaluation for quantum models. nDGC@10 and MAP@10 have been chosen as metrics. QLM-QE has proven to outperform all other models, achieving the result of 10.37%, 15.19% (nDCG@10) and 8.94%, 17.79% (MAP@10) on TREC 2013 and TREC 2014, respectively. The most impressive result has been achieved on TREC 2013, in which there is a 89.81% improvement in MAP@10 score over the baseline and 70.83% compared with the best-performing quantum-model (QMT). However--in spite of these results--further experiments are needed to demonstrate the real benefit of a query expansion approach. In particular, comparisons should be made with classical query expansion approaches and using specific datasets for information retrieval tasks. More recently, [PERSON]. [56] has proposed another improvement to QLM applied to information retrieval by introducing the interference effects in the neural matching model. The work starts from a revision of neural matching models for retrieval, using a probabilistic quantum property. The proposed model--namely the Quantum Interference-inspired Neural Matching Model (QINM)--applies quantum interference theory to the neural matching model for retrieval. In particular, the proposed methodology construct the probability distribution of a document into the reduced density operator, after which the effective probability distribution is extracted using a N-gram window convolution network. Finally, the query attention mechanism calculates matching features and the final matching score is obtained. A set of experiments have been performed to test the effectiveness of the interference matching information. Performances achieved by QINM on ClueWeb-09 and Robust-04 datasets have shown a significant improvements with respect to other QLMs.