arXiv subjects

Olga Tsurkan

Publications and source records attributed to Olga Tsurkan.

3 recordsLinked to original sources

arXiv2025

Photovoltaic power forecasting using quantum machine learning

Accurate forecasting of photovoltaic power is essential for reliable grid integration, yet remains difficult due to highly variable irradiance, complex meteorological drivers, site geography, and device-specific behavior. Although contemporary machine learning has achieved successes, it is not clear that these approaches are optimal: new model classes may further enhance performance and data efficiency. We investigate hybrid quantum neural networks for time-series forecasting of photovoltaic power and introduce two architectures. The first, a Hybrid Quantum Long Short-Term Memory model, reduces mean absolute error and mean squared error by more than 40% relative to the strongest baselines evaluated. The second, a Hybrid Quantum Sequence-to-Sequence model, once trained, it predicts power for arbitrary forecast horizons without requiring prior meteorological inputs and achieves a 16% lower mean absolute error than the best baseline on this task. Both hybrid models maintain superior accuracy when training data are limited, indicating improved data efficiency. These results show that hybrid quantum models address key challenges in photovoltaic power forecasting and offer a practical route to more reliable, data-efficient energy predictions.

cs.LG↗

arXiv2025

Stabilization of industrial processes with time series machine learning

The stabilization of time series processes is a crucial problem that is ubiquitous in various industrial fields. The application of machine learning to its solution can have a decisive impact, improving both the quality of the resulting stabilization with less computational resources required. In this work, we present a simple pipeline consisting of two neural networks: the oracle predictor and the optimizer, proposing a substitution of the point-wise values optimization to the problem of the neural network training, which successfully improves stability in terms of the temperature control by about 3 times compared to ordinary solvers.

cs.LG↗

arXiv2025

Hybrid quantum recurrent neural network for remaining useful life prediction of turbofan engines

Accurate remaining useful life (RUL) estimation underpins safe operation and cost-effective maintenance of aerospace propulsion systems. We propose a Hybrid Quantum Recurrent Neural Network (HQRNN) for jet-engine RUL forecasting on the NASA C-MAPSS FD001 benchmark. The HQRNN stacks Quantum Long Short-Term Memory (QLSTM) layers, replacing each LSTM gate's linear transformation with a Quantum Depth-Infused (QDI) circuit; this is followed by classical dense layers. Quantum and hybrid quantum-classical methods for turbofan RUL prediction are still at an early stage. Our study is therefore among the first to evaluate a gate-based QLSTM hybrid at matched parameter counts, comparing it against classical and joint state-of-the-art models on this benchmark and complementing that comparison with a circuit-level analysis of the quantum layer. Encoding the gate signals in a quantum feature space is intended to help the network represent high-frequency degradation patterns with fewer trainable parameters than a matched classical counterpart. The HQRNN improves mean RMSE and mean MAE by about 5% over matched-parameter stacked-LSTM RNNs across 10 random seeds, and attains a test RMSE of 15.46, outperforming Random Forest, CNN, and MLP baselines. ZX calculus, Fisher information, and Fourier analyses indicate that the QDI circuit is compact, trainable, and expressive. Advanced joint deep-learning models still outperform the stand-alone HQRNN, indicating that quantum-enhanced recurrent modules are best deployed as components within composite prognostics pipelines rather than stand-alone predictors.

cs.LG↗