QnRL is a distributional quantum RL framework that distills conditional action policies from moments of quantum generative models in Hilbert space via the QuAK algorithm, reporting higher scores and fewer parameters than baselines.
Parametrized quantum policies for reinforcement learning,
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quant-ph 2years
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Quantum RL variants with state encoding solve moderate-scale flowsheet synthesis problems competitively with classical RL on per-episode performance and more efficiently per parameter.
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QnRL: Quantum-Native Reinforcement Learning
QnRL is a distributional quantum RL framework that distills conditional action policies from moments of quantum generative models in Hilbert space via the QuAK algorithm, reporting higher scores and fewer parameters than baselines.
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Enhanced Reinforcement Learning-based Process Synthesis via Quantum Computing
Quantum RL variants with state encoding solve moderate-scale flowsheet synthesis problems competitively with classical RL on per-episode performance and more efficiently per parameter.