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.
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.
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.
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.
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.
Abstract Quantum Machine Learning (QML) holds the promise of making significant changes to how Artificial Intelligence (AI) functions. A notable breakthrough in this field is the development of Quantum Long Short- Term Memory (QLSTM) networks, which exhibit faster learning compared to standard Long Short-Term Memory (LSTM) networks. QLSTM builds upon classical LSTM networks but incorporates principles of quantum computation, allowing it to explore superposition and entanglement for parallel information processing. By using quantum bits (qubits), QLSTM potentially offers advantages for specific machine learning tasks. Despite its potential, QLSTM remains a relatively unexplored area within QML, particularly regarding its dependency on the number of qubits and the impact of different optimizers on its performance. This study aims to reveal valuable insights to enhance QLSTM and explore its potential applications in AI. The study focuses on two main aspects. Firstly, exploring how the Adam and Ada-delta optimization methods improve QLSTM models, playing a role in refining the performance of the quantum model. Secondly, the study delves into the consequences of using varying numbers of qubits in the QLSTM model. Understanding how the quantity of qubits influences performance is essential for optimizing the network's efficiency. By addressing these aspects, the study seeks to contribute to advancing QLSTM and offering valuable knowledge for its potential applications in the AI field. The findings may suggest exciting opportunities for future research on QLSTM models, prompting exploration into optimization strategies, regularization techniques, and quantum data augmentation. Additionally, the study emphasizes the importance of leveraging ensemble learning and validating applicability in real-world quantum computing scenarios. These efforts are crucial for advancing understanding and maximizing potential in the continually evolving landscape of quantum computing applications.
Abstract Quantum computing combined with machine learning (ML) is a highly promising research area, with numerous studies demonstrating that quantum machine learning (QML) is expected to solve scientific problems more effectively than classical ML. In this work, we present Quantum Long Short-Term Memory (QLSTM), a QML architecture, and demonstrate its effectiveness in drug discovery. We evaluate QLSTM on five benchmark datasets (BBBP, BACE, SIDER, BCAP37, T-47D), and observe consistent performance gains over classical LSTM, with ROC-AUC improvements ranging from 3% to over 6%. Furthermore, QLSTM exhibits improved predictive accuracy as the number of qubits increases, and faster convergence than classical LSTM under the same training conditions. Notably, QLSTM maintains strong robustness against quantum computer noise, outperforming noise-free classical LSTM in certain settings. These findings highlight the potential of QLSTM as a scalable and noise-resilient model for scientific applications, particularly as quantum hardware continues to advance in qubit capacity and fidelity.
Quantum computing combined with machine learning (ML) is a highly promising research area, with numerous studies demonstrating that quantum machine learning (QML) is expected to solve scientific problems more effectively than classical ML. In this work, we present Quantum Long Short-Term Memory (QLSTM), a QML architecture, and demonstrate its effectiveness in drug discovery. We evaluate QLSTM on five benchmark datasets (BBBP, BACE, SIDER, BCAP37, T-47D), and observe consistent performance gains over classical LSTM, with ROC-AUC improvements ranging from 3% to over 6%. Furthermore, QLSTM exhibits improved predictive accuracy as the number of qubits increases, and faster convergence than classical LSTM under the same training conditions. Notably, QLSTM maintains strong robustness against quantum computer noise, outperforming noise-free classical LSTM in certain settings. These findings highlight the potential of QLSTM as a scalable and noise-resilient model for scientific applications, particularly as quantum hardware continues to advance in qubit capacity and fidelity.