Benchmarking Quantum Reinforcement Learning
Meyer N, Ufrecht C, Yammine G, Kontes G, Mutschler C, Scherer DD (2025)
Publication Type: Conference contribution
Publication year: 2025
Publisher: ML Research Press
Book Volume: 267
Pages Range: 43934-43964
Conference Proceedings Title: Proceedings of Machine Learning Research
Event location: Vancouver, BC, CAN
Abstract
Benchmarking and establishing proper statistical validation metrics for reinforcement learning (RL) remain ongoing challenges, where no consensus has been established yet. The emergence of quantum computing and its potential applications in quantum reinforcement learning (QRL) further complicate benchmarking efforts. To enable valid performance comparisons and to streamline current research in this area, we propose a novel benchmarking methodology, which is based on a statistical estimator for sample complexity and a definition of statistical outperformance. Furthermore, considering QRL, our methodology casts doubt on some previous claims regarding its superiority. We conducted experiments on a novel benchmarking environment with flexible levels of complexity. While we still identify possible advantages, our findings are more nuanced overall. We discuss the potential limitations of these results and explore their implications for empirical research on quantum advantage in QRL.
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Germany (DE)How to cite
APA:
Meyer, N., Ufrecht, C., Yammine, G., Kontes, G., Mutschler, C., & Scherer, D.D. (2025). Benchmarking Quantum Reinforcement Learning. In Aarti Singh, Maryam Fazel, Daniel Hsu, Simon Lacoste-Julien, Felix Berkenkamp, Tegan Maharaj, Kiri Wagstaff, Jerry Zhu (Eds.), Proceedings of Machine Learning Research (pp. 43934-43964). Vancouver, BC, CAN: ML Research Press.
MLA:
Meyer, Nico, et al. "Benchmarking Quantum Reinforcement Learning." Proceedings of the 42nd International Conference on Machine Learning, ICML 2025, Vancouver, BC, CAN Ed. Aarti Singh, Maryam Fazel, Daniel Hsu, Simon Lacoste-Julien, Felix Berkenkamp, Tegan Maharaj, Kiri Wagstaff, Jerry Zhu, ML Research Press, 2025. 43934-43964.
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