Practical application-specific advantage through hybrid quantum computing. - Michael Perelshtein; Asel Sagingalieva; Karan Pinto; Vishal Shete; Alexey I. Pakhomchik; Artem Melnikov; Florian Neukart; Georg Gesek; Alexey Melnikov 003; Valerii M. Vinokur (CoRR, 2022)

Quantum physics-informed neural networks for simulating computational fluid dynamics in complex shapes. - Alexander Sedykh; Maninadh Podapaka; Asel Sagingalieva; Nikita Smertyak; Karan Pinto; Markus Pflitsch; Alexey Melnikov 003 (CoRR, 2023)

Numerical solution of the incompressible Navier-Stokes equations for chemical mixers via quantum-inspired Tensor Train Finite Element Method. - Egor Kornev; Sergey Dolgov; Karan Pinto; Markus Pflitsch; Michael Perelshtein; Artem Melnikov (CoRR, 2023)

Photovoltaic power forecasting using quantum machine learning. - Asel Sagingalieva; Stefan Komornyik; Arsenii Senokosov; Ayush Joshi; Alexander Sedykh; Christopher Mansell; Olga Tsurkan; Karan Pinto; Markus Pflitsch; Alexey Melnikov 003 (CoRR, 2023)

Quantum Algorithms Applied to Satellite Mission Planning for Earth Observation. - Serge Rainjonneau; Igor Tokarev; Sergei Iudin; Saaketh Rayaprolu; Karan Pinto; Daria Lemtiuzhnikova; Miras Koblan; Egor Barashov; Mohammad Kordzanganeh; Markus Pflitsch; Alexey Melnikov 003 (IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2023)

Predictive control of blast furnace temperature in steelmaking with hybrid depth-infused quantum neural networks. - Nayoung Lee; Minsoo Shin; Asel Sagingalieva; Ayush Joshi Tripathi; Karan Pinto; Alexey Melnikov 003 (CoRR, 2025)