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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Pith papers citing it
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quant-ph 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
LPQCs are shown to universally approximate distributions over quantum density operators in 1-Wasserstein distance via a hybrid classical-quantum construction, with added multimodal priors and mixture-of-experts architecture that empirically reduces barren plateaus.
citing papers explorer
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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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Latent-Conditioned Parameterized Quantum Circuits as Universal Approximators for Distributions over Quantum States
LPQCs are shown to universally approximate distributions over quantum density operators in 1-Wasserstein distance via a hybrid classical-quantum construction, with added multimodal priors and mixture-of-experts architecture that empirically reduces barren plateaus.