Improved Quantum Algorithms for Reinforcement Learning Under a Generative Model
A new preprint on arXiv proposes improved quantum algorithms for reinforcement learning that utilize a generative model to achieve lower sample complexity. The algorithms are designed to solve Markov decision processes with provable speedups over classical methods in certain settings.
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What this could mean
- 0–2 yearsPlausible
These algorithmic improvements could enable the demonstration of quantum reinforcement learning on near-term devices for problems with large state spaces, such as simple game environments or control tasks.
If the theoretical sample complexity reductions translate to practical implementations, the lower resource requirements may allow the algorithms to run on existing noisy intermediate-scale quantum (NISQ) devices, opening the door to early empirical results in quantum machine learning.
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