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.
Lan, Variational quantum soft actor-critic
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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.
The paper claims a fully quantum MDP model for RL with quantum state transitions, return calculation, and trajectory search that achieves quantum enhancement.
Survey summarizing performance metrics of fully connected QNNs, quantum CNNs, equivariant QNNs, quantum Hopfield networks, quantum Boltzmann machines, quantum reservoir computing, and composite networks for reinforcement, generative, and transfer learning.
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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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.
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Research progress on quantum neural networks and quantum machine learning
Survey summarizing performance metrics of fully connected QNNs, quantum CNNs, equivariant QNNs, quantum Hopfield networks, quantum Boltzmann machines, quantum reservoir computing, and composite networks for reinforcement, generative, and transfer learning.