Quantum Natural Language Processing: History
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Subjects: Computer Science, Interdisciplinary Applications
Contributor:
Raffaele Guarasci
,
Giuseppe De Pietro
,
Massimo Esposito

Quantum Natural Language Processing (QNLP) is a hybrid field that combines aspects derived from Quantum Computing (QC) with tasks of Natural Language Processing (NLP).

  • quantum computing
  • natural language processing
  • neural language models

1. Introduction

The rapid growth of deep-learning-based neural language models (NLMs) 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 statistics could 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 super-polynomial speedup [18][20][21].

2. NLP and Quantum: The Meeting Point

One of the assumptions underlying the union between natural language processing and quantum theory is the possibility of creating a direct relationship between linguistic features (i.e., syntactic structures and semantics meanings) and quantum states.
This is made possible using the DisCoCat framework through string diagrams [22] as a network-like language [23].
This approach is part of a long and flourishing tradition of computational linguistics focused on the search for the most efficient way to represent language structures and meanings in a machine-readable way. On the one hand, the distributional approach—which has been the most successful line of research in recent years—relies on statistics about the contexts in which words occur according to the distributional hypothesis [24]. By contrast, the symbolic approach [25] has been focused on individual meanings that compose the sentence. This approach is based on the theoretical linguistics’ concept of compositionality, arguing that the meaning of a sentence depends on the meanings of its parts and by the grammar according to which they are arranged together. Therefore, the analysis of the individual constituents determines the overall meaning, which is expressed using a formal logical language. This line of research has obtained less success in NLP applications so far.
Current state-of-the-art neural network models are based on the dominant distributional paradigm. Therefore, this approach is not without problems. First, there is a big bottleneck created by the need for ever larger data sets and parameters; moreover, the interpretation of these models is difficult [26].
The first attempt to overcome the limitations of current NLP models is to include features about the structure of the language (basically syntax) into canonical distributional language models. The resulting model—denoted as DisCoCat—incorporates categorical information and distributional information. Note that this is certainly not a new approach in the field of theoretical and computational linguistics, since its roots lie in the Universal Grammar [27] and foundational work of [28][29], while applied aspects come from categorical grammars proposed by [30] and pregroup grammar [31].

The Compositional Distributional Model

Given the premise that the constituents of a sentence are strongly interconnected, and the grammatical structures in which they are involved affect semantics [32], the pioneering work proposed by [33] has proposed a graphical framework to draw string diagrams (see Figure 1) exploiting concepts from Lambek’s pregroup grammar [34]. The uniqueness of the proposed representation is that sentence meanings can be totally independent of the grammatical structure.
Applsci 12 05651 g001 550
Figure 1. Example of a simple sentence represented using a string diagram inspired by formalism proposed in [33].
The question they intended to answer is not only rooted in compositionality, i.e., whether the meaning of a whole sentence can be deduced by single meanings of its words. The aim is rather to make the first steps towards a grammar-informed NLP, deepening the ways in which words interact with each other and establishing their meanings. In other terms, the framework aims to combine in a whole diagrammatic representation structural aspects of language (grammar theory and syntax) and statistical approaches based on empirical evidences (machine/deep learning).
In the diagram, boxes represent meanings of words that are transmitted via wires. It deals with a representation similar to the canonical Dependency Parse Tree (DPT) well known in the linguistics literature, but it does not introduce a hierarchical tree structure. In the example shown in Figure 1, the noun in subject position “Max” and the one in object position “pizza” are both related with the verb “ate” and the combination of these words builds up the meaning of the overall sentence. In this way, distributional and compositional aspects are combined into DisCoCat. The meaning of sentences is computed using pregroup grammar via tensor product composition. In particular, it is possible to go through the classic DPT using the tensor product of vector spaces of the meanings of words and vectors of their grammatical roles. For instance, the example sentence in Figure 1 can be represented as follows:
(Max−→−−⊗subj−→−⊗ate−→−⊗(pizza−→−−⊗obj−→)
This vector in the tensor product space can be considered as the meaning of the sentence “Max ate pizza”. Subsequently, this model has been reformulated in quantum terms, creating the pregroup only using Bell-effect and identities [35]. In this diagrammatic notation (see Figure 2), pentagons represent quantum states and wires represent the Bell-effect. The equivalence of wire structure with pregroup grammar has been demonstrated [36].
Applsci 12 05651 g002 550
Figure 2. Diagrammatic notation showing how word meaning can be interpreted as quantum states and grammatical structure as quantum measurements.
Notice that the original DisCoCat model works perfectly without any reference to quantum theory, even if its true origin is the categorical quantum mechanics (CQM) formalism [37] and this connection is only made explicit in further work [36].
The novelty in introducing elements from quantum theory lies in the argument put forward in the work of [38] and then elaborated and enriched in [36]: QNLP can be considered “quantum-native” since quantum theory and natural language share an interaction structure and the use of vector spaces. This interaction structure determines the entire structure of processes, including the specification of the spaces where the states live. Vector spaces are used to describe states. This implies that natural language could better fit in a quantum hardware than a classical one.
Hence, the translation of linguistic structure into quantum circuits is particularly suitable to be implemented in a proper quantum hardware (NISQ) and consequently benefit from quantum advantage in terms of speedup.

Different Approaches

QNLP has affected NLP in different ways since the release of the compositional distributional model. Early works have focused on specific linguistic issues and critical tasks of NLP. Subsequently, the focus shifted to more straightforward tasks that can be implemented on actual data and compared with existing benchmarks in the literature. Approaches can be classified as follows:

  1. Theoretical Approaches: First QNLP approaches focused on formal aspects of natural language. These works propose algorithms based on QC for different NLP tasks. In these works, the methodological and performance advantages of QNLP algorithms have been theoretically demonstrated, based on the assumption of the theorized but never realized QRAM. Alternative approaches have been developed to overcome this shortcoming. Variational quantum circuits [36] or classical ansatz parameters[38]  have been tested to encode distributional embedding. 
  2. Quantum-Inspired Approaches: a family of hybrid approaches exploiting quantum properties to address NLP tasks running on classical hardware. These approaches have the advantage that they can be implemented without having to rely on quantum hardware. In addition, these approaches can be tested on actual data and compared with benchmark datasets using classical metrics used to estimate performance. These approaches are mainly based on a density matrix defined in probabilistic quantum space. The density matrix has proven to be an effective way of representing and modeling language in different NLP tasks, encoding more semantic dependencies concerning classic word embeddings. This sub-field has attracted the most interest in the literature since different quantum language models (QLM) have been proposed for different tasks, ranging from Information Retrieval[40], Question Answering \cite, and Sentiment Classification \cite. Comparative visualization of the models proposed by the quantum-inspired works described below, including the datasets on which they have been tested; the performance achieved against the reference baselines are shown in Table 2.
  3. Quantum-Computer Approaches: these approaches have been actually tested on real quantum hardware (NISQ devices CITE). These works are intended as the applied counterpart of theoretical works in which the mathematical foundations are provided. They start from the assumption that a quantum-based model of language should be closer and more reliable than current language models with respect to a specific task. Experiments have focused on simple NLP tasks. In particular, the first implementation of an NLP task on NISQ hardware has been proposed by [49], following theoretical methods proposed in [38]. The conceptual and mathematical foundations on which these works are based are described in [36]. It uses DisCoCat to perform a simple question-answering task on a small custom dataset, adopting the paradigm of Parameterized quantum circuits as machine learning models [50]. Subsequently, in [54], the first medium-scale NLP experiments running on quantum hardware have been performed. Two tasks are proposed, and both of them are structured as binary classification problems. The first one uses a dataset of 130 simple-syntax sentences generated from a fixed vocabulary using a simple CFG that can refer to one of two possible topics. For the second task, 105 noun phrases are extracted from the RelPron dataset [55], and the goal of the model is to predict whether a noun phrase contains a subject-based or an object-based relative clause. Finally, in [56], a preliminary experiment focused on machine translation using DisCoCat has been proposed. The goal of the experiment is the possibility of a quantum-like approach to language understanding in different languages. This is the first work trying to use DisCoCat for a language other than English.
     
Table 2. Comparison of quantum-inspired approaches running on classical hardware. 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.
Task Proposed Approach Dataset Metric Score
Information retrieval Quantum Language Model (QLM) [40] SJMN MAP 0.2093 (0.271)
TREC7-8 0.2254 (0.2243)
WT10g 0.2264 (0.2146)
ClueWeb-B 0.1196 (0.1137)
Quantum Language Model-based
Query Expansion (QLM-QE) [41]		 TREC 2013 MAP@10 8.94 (4.91)
TREC 2014 14.79 (14.52)
Quantum Interference-inspired
Neural Matching model (QINM) [42]		 ClueWeb-B MAP 0.134 (0.082)
Robust-04 0.294 (0.103)
Question answering Neural Network-based Quantum-like
Language Model (NNQLM) [43]		 TREC-QA MAP 0.758 (0.678)
WIKIQA 0.649 (0.510)
Quantum Many-body Wave Function
for Language Modeling (QMWF-LM) [44]		 TREC-QA MAP 0.752 (0.678)
WIKIQA 0.695 (0.512)
Complex-valued-Network (CNM) [45] YahooQA MAP 0.575 (0.395)
TREC-QA 0.770 (0.777)
WIKIQA 0.674 (0.652)
Sentiment classification GQLM [46] OMD Accuracy 0.629 (0.614)
SS-tweet 0.618 (0.579)
Quantum-inspired
Interactive Networks (QIN) [47]		 MELD Accuracy 0.679 (0.652)
IEMOCAP 0.376 (0.351)
TextTN [48] MR Accuracy 82.2 (82.3)
CR 85.7 (85.4)
Subj 95.3 (94.6)
MPQA 90.4 (90.4)
BERT+TextTN [48] SST-2 Accuracy 95.3 (96.7)
SST-5 54.8 (54.7)
 
 

This entry is adapted from the peer-reviewed paper 10.3390/app12115651

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