A dynamic-circuit framework for multi-step quantum Markov decision processes reduces physical qubit count from O(T) to O(1) while preserving trajectory fidelity and applying Grover amplification for high-return paths.
These differ- ences clarify the respective strengths and limitations of each method when deployed on near-term quantum devices
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Scalable Quantum Reinforcement Learning on NISQ Devices with Dynamic-Circuit Qubit Reuse and Grover Optimization
A dynamic-circuit framework for multi-step quantum Markov decision processes reduces physical qubit count from O(T) to O(1) while preserving trajectory fidelity and applying Grover amplification for high-return paths.