A single-qubit quantum reinforcement learning agent solves CartPole faster than classical networks and quantifies shot-count versus control-frequency requirements for real-time closed-loop control on NISQ hardware, including direct electronics programming to reduce latency.
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A quantum echo-state network is implemented on NISQ superconducting qubits and shown to predict long chaotic trajectories from the Lorenz system with memory persisting over 100 times the median T1/T2 time.
Staged KD from a frozen classical visual encoder enables shallow VQC heads to learn non-trivial policies on CartPole Pixels and Acrobot Pixels where direct pixel-to-VQC training is harder.
citing papers explorer
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Towards Real-time Control of a CartPole System on a Quantum Computer
A single-qubit quantum reinforcement learning agent solves CartPole faster than classical networks and quantifies shot-count versus control-frequency requirements for real-time closed-loop control on NISQ hardware, including direct electronics programming to reduce latency.
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Staged Hybridisation for Visual Quantum Reinforcement Learning via Knowledge Distillation
Staged KD from a frozen classical visual encoder enables shallow VQC heads to learn non-trivial policies on CartPole Pixels and Acrobot Pixels where direct pixel-to-VQC training is harder.