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.
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.
Abstract With the rapid development of quantum machine learning, Quantum Long Short-Term Memory (QLSTM) have demonstrated quantum advantages in time series forecasting problems. Currently, it has found applications in fields such as financial market analysis, natural language processing, and weather forecasting, but its accuracy in regression problem prediction still shows deficiencies. To improve the prediction accuracy of the model, enhanced quantum long short-term memory by using bidirectional ring variational quantum circuit (EQLSTM) is proposed. We reconsidered and optimized the types, quantities, and arrangements of quantum gates, designing a bidirectional ring variational quantum circuit composed of CRX gates (Bi-VQC) to enhance its expressibility. Bi-VQC can more accurately describe and encode complex quantum state features, enabling the EQLSTM to better learn and represent the characteristics of the input data. In addition, Bi-VQC reduces the number of optimizable parameters and lowers the circuit load of the EQLSTM model. To validate the effectiveness of the proposed model, experiments are conducted to test and evaluate the EQLSTM using the Aggregated traffic in the United Kingdom academic network backbone. The experimental results confirm that the proposed EQLSTM model improves from 90.56\% to 97.57\% compared to the QLSTM model.
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.
Prediction of stock price remains a challenging task because financial markets are dynamic and often non-linear, unpredictable patterns. To deal with such unpredictable data, the architecture that’s created must identify both time based trends and hidden patterns in data. In this study, we explore a hybrid modelling approach by combining both the classical deep learning techniques with quantum computing principles. Specifically, a Long Short-Term Memory (LSTM) is combined with a Quantum Neural Network (QNN) to understand the benefits of using quantum in time series forecasting. Rather than depending on a single quantum configuration, multiple feature maps and ansatz circuit designs were evaluated to identify the most suitable arrangement for the QLSTM model. Resultant framework blends ideas from both classical computing and quantum approaches to improve how we predict patterns over time in real-life scenarios.
Short-term solar irradiance prediction is critical for efficient power scheduling, the design of solar-integrated energy management systems, and maintaining grid stability. However, the inherent variability of solar energy introduces fluctuations in power output, and posing challenges for reliable grid operation. Leveraging recent advancements in quantum computing, the current work proposes a hybrid quantum-driven neural network for solar irradiance prediction, combining successive variational mode decomposition (SVMD) with an improved quantum long short-term memory (IQLSTM) network. To enhance circuit expressibility, a robust variational quantum circuit (RVQC) incorporating controlled-X (CRX) gates is embedded within the quantum long short-term memory (QLSTM) architecture. In the RVQC, a circular topology with two-way connections is employed to optimize qubit interconnections and strengthen entanglement. The proposed model is evaluated using two solar irradiance datasets of Hyderabad (2019) and Bengaluru (2019, 2018), obtained from the National Renewable Energy Laboratory (NREL). To assess the effectiveness of the proposed circuit, evaluations are carried out under varying weather conditions and across multiple forecasting horizons, and results are compared against classical LSTM and other quantum-based networks like quantum gate recurrent units (QGRUs), quantum support vector machine (QSVM) and QLSTM. Furthermore, the model is evaluated under noisy quantum environments to approximate realistic Noisy Intermediate-Scale Quantum (NISQ) hardware conditions, enabling the assessment of its robustness under practical quantum computing constraints. Experimental results demonstrate that the proposed model achieves superior predictive accuracy and robustness compared with existing classical and quantum-based approaches.
We present the Quantum Kernel-Based Long Short-Term Memory (QK-LSTM) network, which integrates quantum kernel methods into classical LSTM architectures to enhance predictive accuracy and computational efficiency in climate time-series forecasting tasks, such as Air Quality Index (AQI) prediction. By embedding classical inputs into high-dimensional quantum feature spaces, QK-LSTM captures intricate nonlinear dependencies and temporal dynamics with fewer trainable parameters. Leveraging quantum kernel methods allows for efficient computation of inner products in quantum spaces, addressing the computational challenges faced by classical models and variational quantum circuit-based models. Designed for the Noisy Intermediate-Scale Quantum (NISQ) era, QK-LSTM supports scalable hybrid quantum-classical implementations. Experimental results demonstrate that QK-LSTM outperforms classical LSTM networks in AQI forecasting, showcasing its potential for environmental monitoring and resource-constrained scenarios, while highlighting the broader applicability of quantum-enhanced machine learning frameworks in tackling large-scale, high-dimensional climate datasets.
In recent years, the electricity consumption in residential sector has witnessed a significant increase due to the population growth on one hand and the widespread adoption of electrical appliances on the other hand.Hence, finding solutions to decrease electricity consumption has become a matter of great interest for researchers.To address this challenge, we propose a Quantum Cognitive IoT (QCIoT) Framework that integrates quantum deep learning with edge computing to optimize energy use in smart homes.Our key innovation is a hybrid Quantum Long Short-Term Memory (QLSTM) model, which enhances traditional LSTM networks by leveraging quantum circuits for improved timeseries forecasting.Specifically, QLSTM employs parameterized quantum gates to process temporal dependencies more efficiently, enabling higher accuracy than classical approaches.We evaluate quantum-enhanced LSTMs (QLSTMs) against classical LSTM baselines on multivariate time-series forecasting.Experimental results demonstrate that QLSTMs significantly outperform classical counterparts, with the multivariate QLSTM (MQLSTM) achieving a 25.8% reduction in RMSE and improving explanatory power by 78.3%.While QLSTMs exhibit slightly slower convergence, they deliver superior generalization, evidenced by lower test loss and stable training dynamics.These advantages stem from quantum parallelism, entanglement and optimized state representation, which enable superior handling of noisy, high-dimensional smart home data.By integrating quantum-enhanced forecasting with edge-based IoT systems, our framework offers a scalable solution for real-time energy management in smart homes.This work bridges quantum computing and smart infrastructure, demonstrating practical benefits for sustainability and energy savings.