# Quantum Natural Language Processing: Challenges and Opportunities
## 1 Introduction  
In recent years, the explosion of neural language models based on deep learning architecture has led to significant improvement in all NLP tasks [1; 2; 3; 4], ranging from machine translation [5], text classification [6], coreference resolution [7; 8] or multi-language syntactic analysis [9; 10; 11]. In particular, Transformers-based models such as BERT have proved to outperform previous generation state-of-the-art architecture such as Long Short-Term Memory (LSTM) recurrent neural networks (RNN). However, the improvement in performance is matched by an increasing complexity of models that have led to a paradox. Models require a huge amount of data to be efficiently trained, with an enormous cost in time, resources and computation. This is the major drawback of current approaches based on Transformers, for instance the number of parameters for this kind of neural networks reaches the order of hundreds of billions (data referred to OpenAI GPT model) [12; 13]. In addition, it requires big resources for the training phase (e.g., the whole Wikipedia corpus in several languages). Beyond to these aspects, there are also open issues inherent to what really these models learn about language [14; 15], how they encode this information [16] and how much of the information learned is really interpretable [17]. The literature has produced several studies focused on whether neural language models are able to encode a sort of linguistic information or whether they just replicate patterns observed in written texts. An alternative way that is gaining attention in recent years is that which originates from quantum computing, in particular quantum-machine learning sub-field. The idea is to exploit powerful aspects borrowed from quantum mechanics to overcome computational limitations of current approaches [18]. The dominant paradigm of classical statisticscould be extended using quantum mechanics by representing objects with matrices of complex numbers. In quantum computing, bits are replaced by qubits, which are able to handle information in a non-binary state using a property of quantum system called superposition [19]. Quantum algorithms can perform calculations with smaller complexity compared to the classical approaches using an intrinsic property of qubits known as superpolynomial speedup [18; 20; 21]. Recent work has observed the development of quantum algorithms that could serve as foundations for machine learning applications. In some cases, quantum properties have been used to simply improve performance of machine learning approaches, while in others problems of machine learning have been reformulated using quantum theory [22; 23]. Process classical data using machine learning algorithms using quantum systems has generated a big strand of research. Quantum machine learning has been used for different purposes: delving into the use of quantum phenomena for learning systems, exploring the ability of quantum computers to learn on quantum data and the possibility to reformulate and implement machine learning algorithms on quantum hardware. As happened before in the case of the classic machine learning, this rapid growth of quantum machine learning algorithms, both from hardware and software side (in terms of devices and algorithms) has involved the natural language processing (NLP) field. This has given rise to the so-called QuantumNLP (QNLP) [24], defined as the implementation of natural language on quantum hardware. It matches the compositional language structure (grammar and semantics) with composition made possible by quantum systems in order to model natural language and perform simple NLP tasks exploiting algorithms derived from the quantum machine learning. The core idea at the basis of QNLP is that the most effective manner for bringing language meaning and grammatical structure together was provided by the categorical quantum mechanics framework. Moreover, it promises to be more aware of the meanings of the language involved in the modeling.