Variational Quantum Circuits in Offline Contextual Bandit Problems
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911144581529600 |
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| author | Schulte, Lukas Hein, Daniel Udluft, Steffen Runkler, Thomas A. |
| author_facet | Schulte, Lukas Hein, Daniel Udluft, Steffen Runkler, Thomas A. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_07633 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Variational Quantum Circuits in Offline Contextual Bandit Problems Schulte, Lukas Hein, Daniel Udluft, Steffen Runkler, Thomas A. Quantum Physics Artificial Intelligence 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. |
| title | Variational Quantum Circuits in Offline Contextual Bandit Problems |
| topic | Quantum Physics Artificial Intelligence |
| url | https://arxiv.org/abs/2509.07633 |