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

Accurately forecasting solar power generation is crucial in the global progression towards sustainable energy systems. In this study, we conduct a meticulous comparison between Quantum Long Short-Term Memory (QLSTM) and classical Long Short-Term Memory (LSTM) models for solar power production forecasting. Our controlled experiments reveal promising advantages of QLSTMs, including accelerated training convergence and substantially reduced test loss within the initial epoch compared to classical LSTMs. These empirical findings 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. This pioneering work provides initial evidence substantiating quantum advantages over classical LSTM, while acknowledging present limitations. Through rigorous benchmarking grounded in real-world data, our study elucidates a promising trajectory for quantum learning in renewable forecasting. Additional research and development can further actualize this potential to achieve unprecedented accuracy and reliability in predicting solar power generation worldwide.

Analogue Radio over Fiber aided Multi-service Communications for High Speed Trains

High speed trains (HST) have gradually become an essential means of transportation, where given our digital world, it is expected that passengers will be connected all the time. More specifically, the on-board passengers require fast mobile connections, which cannot be provided by the currently implemented cellular networks. Hence, in this article, we propose an analogue radio over fiber (A-RoF) aided multi-service network architecture for high-speed trains, in order to enhance the quality of service as well as reduce the cost of the radio access network (RAN). The proposed design can simultaneously support sub- 6GHz as well as milimeter wave (mmWave) communications using the same architecture. Explicitly, we design a photonics aided beamforming technique in order to eliminate the bulky high-speed electronic phase-shifters and the hostile broadband mmWave mixers while providing a low-cost RAN solution. Finally, a beamforming range of 180 is demonstrated with a high resolution using our proposed system.