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.
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The paper claims a fully quantum MDP model for RL with quantum state transitions, return calculation, and trajectory search that achieves quantum enhancement.
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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.
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Quantum framework for Reinforcement Learning: Integrating Markov decision process, quantum arithmetic, and trajectory search
The paper claims a fully quantum MDP model for RL with quantum state transitions, return calculation, and trajectory search that achieves quantum enhancement.