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Hybrid quantum cycle generative adversarial network for small molecule generation
The contemporary drug design process demands considerable time and resources to develop each new compound entering the market.
Photovoltaic power forecasting using quantum machine learning
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.
Hybrid quantum physics-informed neural networks for simulating computational fluid dynamics in complex shapes
Finding the distribution of the velocities and pressures of a fluid (by solving the Navier-Stokes equations) is a principal task in the chemical, energy, and pharmaceutical industries, as well as in mechanical engineering and the design of pipeline systems.
Quantum algorithms applied to satellite mission planning for Earth observation
This paper introduces a set of quantum algorithms to solve the mission planning problem and demonstrate an advantage over the classical algorithms implemented thus far.
Benchmarking simulated and physical quantum processing units using quantum and hybrid algorithms
This study paves the way to understanding the optimal combination of available software and hardware for executing practical quantum algorithms.
