QNLP in Practice: Running Compositional Models of Meaning on a Quantum Computer

Abstract

Quantum Natural Language Processing (QNLP) deals with the design and implementation of NLP models intended to be run on quantum hardware. In this paper, we present results on the first NLP experiments conducted on Noisy Intermediate-Scale Quantum (NISQ) computers for datasets of size greater than 100 sentences. Exploiting the formal similarity of the compositional model of meaning by Coecke, Sadrzadeh, and Clark (2010) with quantum theory, we create representations for sentences that have a natural mapping to quantum circuits. We use these representations to implement and successfully train NLP models that solve simple sentence classification tasks on quantum hardware. We conduct quantum simulations that compare the syntax-sensitive model of Coecke et al. with two baselines that use less or no syntax; specifically, we implement the quantum analogues of a “bag-of-words” model, where syntax is not taken into account at all, and of a word-sequence model, where only word order is respected. We demonstrate that all models converge smoothly both in simulations and when run on quantum hardware, and that the results are the expected ones based on the nature of the tasks and the datasets used. Another important goal of this paper is to describe in a way accessible to AI and NLP researchers the main principles, process and challenges of experiments on quantum hardware. Our aim in doing this is to take the first small steps in this unexplored research territory and pave the way for practical Quantum Natural Language Processing.

Hybrid Classical-Quantum Transfer Learning for Text Classification

“…Lambeq [11] is a Python library for natural language processing, based on the compositional model DiscoCat [12]. This model can capture the compositional nature of natural language with the help of category theory.…”

Proceedings of the 15th International Conference on Natural Language Generation

“…This transforms each DisCoCat diagram into an Insantanoues Quantum Polynomial (IQP) circuit. We do not justify this choice of ansatz here, more information is available in (Havlíček et al, 2019;Lorenz et al, 2021). The parameterised quantum circuit corresponding to "Alice generates language" is given in figure 3.…”

“…What we outline here is a step-by-step overview for solving the following task: Given a dataset Γ of sentences, each of which belongs to one of k possible topics, train a classifier that can correctly determine the topic of further unseen sentences (provided the unseen sentences are also about one of the k possible topics). This section mostly follows Lorenz et al (2021), although we modify the algorithm to perform multi-class rather than binary sentence classification.…”

“…the algorithmic generation of data, and Natural Language Processing (NLP), both of which are active research topics within the quantum software community (see e.g. Wootton, 2020b,a;Coecke et al, 2020;Lorenz et al, 2021). The importance of NLG is underscored by its wide range of potential applications.…”

“…Throughout this work we will make use of the well-established mathematical connection between the Distributional Compositional Categorical (Dis-CoCat) (Coecke et al, 2010) model of natural language and quantum theory. This connection was recently exploited in several works (Meichanetzidis et al, 2020;Lorenz et al, 2021) to successfully perform Quantum Natural Language Processing (QNLP) on real quantum hardware (as opposed to simulation with conventional hardware). More specifically it was used to perform the task of binary sentence classification.…”