Variational Quantum Circuits in Offline Contextual Bandit Problems
This work addresses industrial optimization tasks, but it is incremental as it applies existing quantum methods to a new domain.
The paper tackled offline contextual bandit problems in industrial optimization by applying variational quantum circuits, showing that quantum models can effectively fit reward functions and generalize in noisy datasets, providing a proof of concept for their use.
This paper explores the application of variational quantum circuits (VQCs) for solving offline contextual bandit problems in industrial optimization tasks. Using the Industrial Benchmark (IB) environment, we evaluate the performance of quantum regression models against classical models. Our findings demonstrate that quantum models can effectively fit complex reward functions, identify optimal configurations via particle swarm optimization (PSO), and generalize well in noisy and sparse datasets. These results provide a proof of concept for utilizing VQCs in offline contextual bandit problems and highlight their potential in industrial optimization tasks.