A two-armed bandit's reward probabilities are learned as quantum-circuit rotations on IBM hardware and then used in quantum policy evaluation on an IonQ trapped-ion machine, with noisy but partly correct results.
Quantum reinforcement learning via policy iteration,
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From Classical Data to Quantum Advantage -- Quantum Policy Evaluation on Quantum Hardware
A two-armed bandit's reward probabilities are learned as quantum-circuit rotations on IBM hardware and then used in quantum policy evaluation on an IonQ trapped-ion machine, with noisy but partly correct results.