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International Conference on Information and Communication Technology Convergence (ICTC)

Advancing Textual Semantic Understanding with Quantum LSTM Networks

Muhammad Mustafa Umar Gondel, Kyung Min Byun, Hyundong Shin

Published Oct 16, 2024• 2 citations
Publisher

Abstract

Long Short-Term Memory (LSTM) networks are adept at managing long-term dependencies in sequential data, making them particularly effective for understanding semantics in context. However, challenges such as language complexity and data limitations persist in natural language processing (NLP). Quantum computing utilizes quantum mechanics to tackle complex language structures with limited data. By integrating quantum computing with LSTM, we can address the issues of linguistic complexity and data scarcity. In this paper, we present a Quantum Long Short-Term Memory (QLSTM) network that comprehends the semantics of textual data. Our simulations demonstrate superior capability of QLSTM in capturing textual semantics.

DOI
10.1109/ictc62082.2024.10826708

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Long Short-Term Memory (LSTM) networks are adept at managing long-term dependencies in sequential data, making them particularly effective for understanding semantics in context. However, challenges such as language complexity and data limitations persist in natural language processing (NLP). Quantum computing utilizes quantum mechanics to tackle complex language structures with limited data. By integrating quantum computing with LSTM, we can address the issues of linguistic complexity and data scarcity. In this paper, we present a Quantum Long Short-Term Memory (QLSTM) network that comprehends the semantics of textual data. Our simulations demonstrate superior capability of QLSTM in capturing textual semantics.
Traditional machine learning (ML) and deep learning (DL) models have shown effectiveness in natural language processing (NLP) tasks, such as sentiment analysis.However, they often struggle with complex linguistic structures, such as sarcasm and implicit claims.This paper introduces a Quantum Long Short-Term Memory (QLSTM) framework for detecting sarcasm and identifying claims in text, aiming to enhance the analysis of complex sentences.We evaluate four approaches: (1) classical LSTM, (2) quantum framework using QLSTM, (3) voting ensemble combining classical and quantum LSTMs, and (4) hybrid framework integrating both types.The experimental results indicate that the QLSTM approach excels in sarcasm detection, while the voting framework performs best in claim identification.
Quantum machine learning (QML) offers new perspectives for modeling complex sequential data by leveraging the computational advantages of quantum computing. In this work, we explore the application of Quantum Long Short-Term Memory (QLSTM) networks for sequence prediction tasks. By integrating parameterized quantum circuits within the classical LSTM architecture, QLSTM models exploit quantum parallelism and entanglement to learn temporal dependencies in data more efficiently. We evaluate the performance of QLSTM on synthetic and real-world sequence prediction tasks and compare it with its classical counterpart. The results demonstrate that QLSTM models achieve competitive performance with fewer trainable parameters, indicating their potential as lightweight and efficient models for sequence learning and prediction.
Abstract The rapid evolution of artificial intelligence has driven interest in Long Short-Term Memory (LSTM) networks for their effectiveness in processing sequential data. However, traditional LSTMs are limited by issues such as the vanishing gradient problem and high computational demands. Quantum computing offers a potential solution to these challenges, promising advancements in computational efficiency through the unique properties of qubits, such as superposition and entanglement. This paper presents a theoretical analysis and an implementation plan for a Quantum LSTM (qLSTM) model, which seeks to integrate quantum computing principles with traditional LSTM networks. While the proposed model aims to address the limitations of classical LSTMs, this study focuses primarily on the theoretical aspects and the implementation framework. The actual architecture and its practical effectiveness in enhancing sequential data processing remain to be developed and demonstrated in future work.
The integration of quantum computing into classical machine learning architectures has emerged as a promising approach to enhance model efficiency and computational capacity. In this work, we introduce the Quantum Kernel-Based Long Short-Term Memory (QK-LSTM) network, which utilizes quantum kernel functions within the classical LSTM framework to capture complex, non-linear patterns in sequential data. By embedding input data into a high-dimensional quantum feature space, the QK-LSTM model reduces the reliance on large parameter sets, achieving effective compression while maintaining accuracy in sequence modeling tasks. This quantum-enhanced architecture demonstrates efficient convergence, robust loss minimization, and model compactness, making it suitable for deployment in edge computing environments and resource-limited quantum devices (especially in the NISQ era). Benchmark comparisons reveal that QK-LSTM achieves performance on par with classical LSTM models, yet with fewer parameters, underscoring its potential to advance quantum machine learning applications in natural language processing and other domains requiring efficient temporal data processing.
Long short-term memory (LSTM) is a kind of recurrent neural networks (RNN) for sequence and temporal dependency data modeling and its effectiveness has been extensively established. In this work, we propose a hybrid quantum-classical model of LSTM, which we dub QLSTM. We demonstrate that the proposed model successfully learns several kinds of temporal data. In particular, we show that for certain testing cases, this quantum version of LSTM converges faster, or equivalently, reaches a better accuracy, than its classical counterpart. Due to the variational nature of our approach, the requirements on qubit counts and circuit depth are eased, and our work thus paves the way toward implementing machine learning algorithms for sequence modeling such as natural language processing, speech recognition on noisy intermediate-scale quantum (NISQ) devices.
As one of the well-known methods to solve natural language processing (NLP) problems, the long short-term memory neural network (LSTM) has been designed to implement on quantum computers. However, existing method requires the number of qubits is equal to the dimension of the word vectors, and may result in a surge of the qubit number. To address this problem, we propose a duplication-free quantum long short-term memory neural network (DQLSTM). Specifically, our DQLSTM adopts the amplitude encoding method to store the classical information, and further can reduce the requirement for the number of qubits. Our numerical results show that our DQLSTM has similar effectiveness to the classical counterpart in a Chinese sentiment analysis task, and outperforms several previous proposals in the same task.