QCSE: A Pretrained Quantum Context-Sensitive Word Embedding for Natural Language Processing

TL;DR

A pretrained quantum context-sensitive embedding model that captures context-sensitive word embeddings, leveraging the unique properties of quantum systems to learn contextual relationships in languages is presented.

Abstract

Quantum Natural Language Processing (QNLP) offers a novel approach to encoding and understanding the complexity of natural languages through the power of quantum computation. This paper presents a pretrained quantum context-sensitive embedding model, called QCSE, that captures context-sensitive word embeddings, leveraging the unique properties of quantum systems to learn contextual relationships in languages. The model introduces quantum-native context learning, enabling the utilization of quantum computers for linguistic tasks. Central to the proposed approach are innovative context matrix computation methods, designed to create unique, representations of words based on their surrounding linguistic context. Five distinct methods are proposed and tested for computing the context matrices, incorporating techniques such as exponential decay, sinusoidal modulation, phase shifts, and hash-based transformations. These methods ensure that the quantum embeddings retain context sensitivity, thereby making them suitable for downstream language tasks where the expressibility and properties of quantum systems are valuable resources. To evaluate the effectiveness of the model and the associated context matrix methods, evaluations are conducted on both a Fulani corpus, a low-resource African language, dataset of small size and an English corpus of slightly larger size. The results demonstrate that QCSE not only captures context sensitivity but also leverages the expressibility of quantum systems for representing rich, context-aware language information. The use of Fulani further highlights the potential of QNLP to mitigate the problem of lack of data for this category of languages. This work underscores the power of quantum computation in natural language processing (NLP) and opens new avenues for applying QNLP to real-world linguistic challenges across various tasks and domains.

Figures (7)

• Figure 1: QCSE architecture and training pipeline. The model combines: (1) a quantum context-encoding circuit, and (2) a parameterized quantum circuit trained to generate context-sensitive embeddings. Step 1: Define the context {natural, language, is, awesome} and identify the center word (here, processing). The context is transformed into a context matrix. Step 2: Encode the matrix using the context encoding circuit. Context matrices are reshaped for quantum encoding (no resizing required when columns match qubit count). The flexible design adapts to diverse embedding approaches. Step 3: The model predicts the quantum embedding of the center word. Steps 4–6: Compute the loss against the true embedding of the center word and optimize ansatz parameters.
• Figure 2: Architecture of one layer of the quantum context-encoding circuit.
• Figure 3: Architecture of one layer of the parameterized quantum circuit.
• Figure 4: Accuracy versus trainable parameter count in QCSE using Exponential Decay Sinusoidal Method on English and Fulani datasets.
• Figure 5: Accuracy versus trainable parameter count in QCSE. The number of trainable parameters influences performance.

Theorems & Definitions (4)

• Definition 1
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