Photovoltaic power forecasting using quantum machine learning

no code implementations • 27 Dec 2023 • Asel Sagingalieva, Stefan Komornyik, Arsenii Senokosov, Ayush Joshi, Alexander Sedykh, Christopher Mansell, Olga Tsurkan, Karan Pinto, Markus Pflitsch, Alexey Melnikov

Predicting solar panel power output is crucial for advancing the energy transition but is complicated by the variable and non-linear nature of solar energy.

Quantum Machine Learning Time Series +1

Quantum machine learning for image classification

no code implementations • 18 Apr 2023 • Arsenii Senokosov, Alexandr Sedykh, Asel Sagingalieva, Basil Kyriacou, Alexey Melnikov

This model demonstrated a record-breaking classification accuracy of 99. 21% on the full MNIST dataset, surpassing the performance of known quantum-classical models, while having eight times fewer parameters than its classical counterpart.

Classification Efficient Neural Network +3