# Quantum Long Short-Term Memory (QLSTM) vs Classical LSTM in Time Series Forecasting: A Comparative Study in Solar Power Forecasting

arXiv: https://arxiv.org/abs/2310.17032 · HTML: https://mlsanity.com/paper/2310.17032/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 29 references that are in the map.
- First posted on arXiv 25 Oct 2023.

## Builds on (most cited first, 8 of 8)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Variational Quantum Algorithms](https://mlsanity.com/paper/2012.09265/) (2020)
- [Quantum Machine Learning](https://mlsanity.com/paper/1611.09347/) (2016)
- [Challenges and Opportunities in Quantum Machine Learning](https://mlsanity.com/paper/2303.09491/) (2023)
- [Quantum Long Short-Term Memory](https://mlsanity.com/paper/2009.01783/) (2020)
- [Recurrent Quantum Neural Networks](https://mlsanity.com/paper/2006.14619/) (2020)
- [Machine learning & artificial intelligence in the quantum domain](https://mlsanity.com/paper/1709.02779/) (2017)
- [A Novel Stochastic LSTM Model Inspired by Quantum Machine Learning](https://mlsanity.com/paper/2305.10212/) (2023)

## Built on by (most cited first, 15 of 15)

- [QKAN-LSTM: Quantum-inspired Kolmogorov-Arnold Long Short-term Memory](https://mlsanity.com/paper/2512.05049/) (2025)
- [A Privacy-Preserving Federated Framework with Hybrid Quantum-Enhanced Learning for Financial Fraud Detection](https://mlsanity.com/paper/2507.22908/) (2025)
- [Benchmarking Quantum and Classical Sequential Models for Urban Telecommunication Forecasting](https://mlsanity.com/paper/2508.04488/) (2025)
- [Parameter-Efficient Quantum-Inspired Fast Weight Programmers for Traffic-Matrix Forecasting](https://mlsanity.com/paper/2606.27821/) (2026)
- [Toward Practical Quantum Machine Learning: A Novel Hybrid Quantum LSTM for Fraud Detection](https://mlsanity.com/paper/2505.00137/) (2025)
- [Comparative Analysis of QNN Architectures for Wind Power Prediction: Feature Maps and Ansatz Configurations](https://mlsanity.com/paper/2506.14795/) (2025)
- [Recursive QLSTM with Dynamic Variational Quantum Circuit Adaptation](https://mlsanity.com/paper/2606.24932/) (2026)
- [Quantum RNNs and LSTMs Through Entangling and Disentangling Power of Unitary Transformations](https://mlsanity.com/paper/2505.06774/) (2025)
- [Q-DPTS: Quantum Differentially Private Time Series Forecasting via Variational Quantum Circuits](https://mlsanity.com/paper/2508.05036/) (2025)
- [Stable Self-Modulating Quantum Fast-Weight Programmers with Bounded Memory Gates](https://mlsanity.com/paper/2607.02363/) (2026)
- [Quantum-Assisted Machine Learning Models for Enhanced Weather Prediction](https://mlsanity.com/paper/2503.23408/) (2025)
- [Hybrid Quantum Temporal Convolutional Networks](https://mlsanity.com/paper/2602.23578/) (2026)
- [A Quantum-Classical Hybrid Framework for Multivariate Time-Series Forecasting Complexity-Fidelity Trade-offs and Limitations](https://mlsanity.com/paper/2607.16358/) (2026)
- [QuanTiMedAI: Quantum-Enhanced Time-Series Model guided by Agentic AI for Cardiac Arrest Mortality Prediction](https://mlsanity.com/paper/2608.06294/) (2026)
- [Seasonal and Quantum-inspired Models for Neutron Monitor Time Series Forecasting](https://mlsanity.com/paper/2609.30281/) (2026)

## Abstract (arXiv)

Accurate solar power forecasting is pivotal for the global transition towards sustainable energy systems. This study conducts a meticulous comparison between Quantum Long Short-Term Memory (QLSTM) and classical Long Short-Term Memory (LSTM) models for solar power production forecasting. The primary objective is to evaluate the potential advantages of QLSTMs, leveraging their exponential representational capabilities, in capturing the intricate spatiotemporal patterns inherent in renewable energy data. Through controlled experiments on real-world photovoltaic datasets, our findings reveal promising improvements offered by QLSTMs, including accelerated training convergence and substantially reduced test loss within the initial epoch compared to classical LSTMs. These empirical results demonstrate QLSTM's potential to swiftly assimilate complex time series relationships, enabled by quantum phenomena like superposition. However, realizing QLSTM's full capabilities necessitates further research into model validation across diverse conditions, systematic hyperparameter optimization, hardware noise resilience, and applications to correlated renewable forecasting problems. With continued progress, quantum machine learning can offer a paradigm shift in renewable energy time series prediction, potentially ushering in an era of unprecedented accuracy and reliability in solar power forecasting worldwide. This pioneering work provides initial evidence substantiating quantum advantages over classical LSTM models while acknowledging present limitations. Through rigorous benchmarking grounded in real-world data, our study illustrates a promising trajectory for quantum learning in renewable forecasting.