Towards Surrogate Based Dequantization of Quantum Reinforcement Learning
In recent years, the utility of parameterized quantum circuits as function approximators has been widely studied. In the context of reinforcement learning, this approach has led to variational quantum algorithms such as quantum Q-learning. While these methods show promising empirical results, and can provide provable advantages for artificial problems, it remains unclear whether they can provide a provable quantum advantage over classical approaches for problems of practical relevance. A natural way to investigate this question is through the lens of dequantization: The construction of efficient classical algorithms capable of matching the performance of quantum variational methods. Build...
| Authors | Pablo Rodriguez-Grasa, Sofiene Jerbi, Mikel Sanz et al. |
|---|---|
| Primary category | quant-ph |
| Published | 14 September 2026 |
| Last updated at source | 14 September 2026 |
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