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Deep quantum learning with long short-term memory for geodetic time series prediction: Application to length of day prediction

Mostafa Kiani Shahvandi, Junyang Gou, Benedikt S. Soja

Published Oct 15, 2021• 3 citations
Publisher

Abstract

Deep quantum learning is a relatively new concept in which quantum computing algorithms and/or devices are used to enhance the performance of deep learning approaches. Quantum technology originally requires quantum devices, also called quantum computers, which are specially designed computers with hardware parts built upon the concepts of quantum mechanics. Quantum devices are not widely available, but quantum algorithms are increasingly gaining more attention. These algorithms use theoretical considerations of quantum mechanics, including concepts of superposition, entanglement, and interference. Quantum algorithms have shown tremendous speedup and efficiency over traditional methods in many fundamental tasks such as prime factorization and list search. As a result of the power of quantum algorithms, they are used in deep learning approaches to increase their performance. The combined approach, which is normally called deep quantum learning, has shown competitive accuracy with respect to standard deep learning approaches. In order to take advantage of quantum algorithms for sequential data modelling, one needs to combine them with a suitable machine learning model, such as Long Short-Term Memory (LSTM) neural networks. In this study, we introduce quantum LSTM neural networks to time series prediction tasks in geodetic science. We present a special architecture consisting of three layers of LSTM, each having a quantum circuit with two qubits, and a final dense layer with a linear activation function. As an application, we focus on the ultra-short-term prediction of length of day based on geodetic and geophysical time series. We show that these networks can predict length of day better than the state-of-the-art statistical and machine learning methods, especially in the final days of the ten-day prediction window. In a test reflecting conditions of the EOP Prediction Comparison Campaign, the prediction accuracy was 0.024, 0.045, and 0.086 ms for the one-, five-, and ten-day-ahead predictions.

DOI
10.1002/essoar.10508301.1

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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.
Time series forecasting is vital in many fields, including energy demand prediction and financial markets. Long Short-Term Memory (LSTM) networks have demonstrated strong performance in capturing temporal dependencies in sequential data. However, traditional LSTM face challenges such as vanishing gradients and limited capabilities in modeling long-term dependencies, especially with complex, high-dimensional data. The model integrates Variable Quantum Circuit (VQC) into a modified LSTM (mLSTM) structure, taking advantage of quantum properties such as superposition and entanglement to improve memory retention and computational efficiency. We would like to introduce modified Quantum Long Short-Term Memory (mQLSTM), which is specifically designed for photovoltaic (PV) energy forecasting. Key improvements include a quantum memory mechanism that mitigates loss of information over extended sequences, a redesigned forget gate to enhance long-term learning and an Exponentially Weighted Feature (EWF) layer to accelerate convergence and enhance generalization. This paper highlights the potential of quantum-enhanced deep learning architectures to advance time series forecasting and opens new directions for practical applications in renewable energy and beyond.
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 widely used artificial neural network that is well suited for time series prediction. Quantum machine learning as a new research topic combines the advantages of quantum data processing and classical machine learning. In this paper, based on a hybrid quantum classical scheme, we design a quantum enhanced LSTM model and several variants such as QGRU. We also performed experiments with a multivariate time series prediction problem to verify the feasibility of these models. Through this research, we expect to explore the benefits and implementation of quantum-based machine learning.
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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.