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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. [ABSTRACT FROM AUTHOR]

Among the numerous techniques followed to learn a linear classifier through the discriminative dictionary and sparse representations learning of signals, the techniques to learn a nonparametric Bayesian classifier jointly and discriminately with the dictionary and the corresponding sparse representations have drawn considerable attention from researchers. These techniques jointly learn two sets of sparse representations, one for the training samples over the dictionary and the other for the corresponding labels over the dictionary classifier. At the prediction stage, the representations of the test samples computed over the learned dictionary do not truly represent the corresponding labels, exposing weakness in the joint learning claim of these techniques. We mitigate this problem and strengthen the joint by learning a set of weights over the dictionary to represent the training data and further optimizing the same weights over the dictionary classifier to represent the labels of the corresponding classes of the training data. Now, at the prediction stage, the representation weights of the test samples computed over the learned dictionary also represent the labels of the corresponding classes of the test samples, resulting in the accurate reconstruction of the labels of the classes by the learned dictionary classifier. Overall, a reduction in the size of the Bayesian model's parameters also improves training time. We analytically and nonparametrically derived the posterior conditional probabilities of the model from the overall joint probability of the model using Bayes' theorem. We used the Gibbs sampler to solve the joint probability of the model using the derived conditional probabilities, which also supports our claim of efficient optimization of the coupled/joint dictionaries and the sparse representation parameters. We demonstrated the effectiveness of our approach through experiments on the standard datasets, i.e., the Extended YaleB and AR face databases for face recognition, Caltech-101 and Fifteen Scene Category databases for categorization, and UCF sports action database for action recognition. We compared the results with the state-of-the-art methods in the area. The classification accuracies, i.e., 93.25%, 89.27%, 94.81%, 98.10%, and 95.00%, of our approach on the datasets have increases of 0.5 to 2% on average. The overall average error margin of the confidence intervals in our approach is 0.24 compared with the second-best approach, JBDC, for which it is 0.34. The AUC–ROC scores of our approach are 0.98 and 0.992, which are better than those of others, i.e., 0.960 and 0.98, respectively. Our approach is also computationally efficient. [ABSTRACT FROM AUTHOR]

Diversity in design is an important factor that enables developers to explore techniques aiming to reduce cost, complexity, and manufacturability. The optical window around 2000 nm is attracting rapid research considerations for the next generation optical networks as a continuation of C‐, L‐, and U‐bands. Optical amplifier is considered to be one of the major research directions in this optical window. Holmium‐doped fiber amplifier is an appropriate component to amplify signals in this optical window. But, the major issue is the pump laser for pumping the Holmium‐doped fiber (HDF) is that it is costly and not readily procurable. In a pioneering design, the authors introduced a method to pump the HDF using traditional 1480 nm pump diode by taking the advantage of cascaded lasers which were made of Erbium‐doped fiber (EDF) and Thulium‐doped fiber (TDF). In this paper, we propose an alternate method for pumping the HDF using commercially available 1480 nm pump diode by exploiting cascaded lasers implemented using two pairs of reflective fiber Bragg gratings (FBGs) and pieces of EDF and TDF. The EDF in the first laser is excited with a standard 1480 nm pump diode to create 1610 nm continuous wave (CW) laser which is used to excite the TDF in the second laser to generate 1950 nm CW laser that is used to pump the HDF. Peak gain of 40.5, 36, and 23 dB can be attained corresponding to –30, –15, and 0 dBm signal powers, respectively at 2055.6 nm signal wavelength. Output powers of 10.5, 21, and 23 dBm are obtained for –30, –15, and 0 dBm signal powers, respectively at 2055.6 nm signal wavelength. Noise figures (NFs) of 5, 4.2, and 4 dB are observed at signal wavelength of 2066.7 nm for signal powers of −30, −15, and 0 dBm, respectively. Penalty of 4.5 dB has been observed both in peak gain and power at 2055.6 nm signal wavelength for signal power of −15 dBm considering the pair induced quenching. Finally, the proposed alternate pumping scheme is compared with the previous pumping scheme in terms of 3 dB gain bandwidth and NF. It has been observed that 88% increase in 3 dB gain bandwidth and 19% decrease in NF at a signal wavelength of 2044.4 nm for signal power of –30 dBm is noticed in the proposed pumping scheme. [ABSTRACT FROM AUTHOR]