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A Survey on Quantum Reinforcement Learning
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Quantum reinforcement learning is an emerging field at the intersection of quantum computing and machine learning. While we intend to provide a broad overview of the literature on quantum reinforcement learning - our interpretation of this term will be clarified below - we put particular emphasis on recent developments. With a focus on already available noisy intermediate-scale quantum devices, these include variational quantum circuits acting as function approximators in an otherwise classical reinforcement learning setting. In addition, we survey quantum reinforcement learning algorithms based on future fault-tolerant hardware, some of which come with a provable quantum advantage. We provide both a birds-eye-view of the field, as well as summaries and reviews for selected parts of the literature.
Forward citations
Cited by 7 Pith papers
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Complexity scaling and optimal policy degeneracy in quantum reinforcement learning via analytically solvable unitary-control-then-measure models
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Quantum Algorithms for Bandits with Knapsacks with Improved Regret and Time Complexities
Quantum algorithms for bandits with knapsacks achieve improved regret and time complexity by replacing classical sampling with quantum Monte Carlo and approximate quantum LP solving.
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Quantum AIXI: Universal Intelligence via Quantum Information
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Q-SpiRL: Quantum Spiking Reinforcement Learning for Adaptive Robot Navigation
QSNN agent in Q-SpiRL framework achieves up to 99% success rate with efficient paths in 20x20 to 40x40 grid worlds with static and dynamic obstacles, outperforming tabular Q-learning, MLP, SNN, and QMLP baselines unde...
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Quantum Reinforcement Learning by Adaptive Non-local Observables
Adaptive non-local observables, jointly trained with variational circuit parameters, improve DQN and A3C reinforcement learning agents on simulated benchmark tasks relative to fixed Pauli-measurement baselines.
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Quantum computing and artificial intelligence: status and perspectives
A broad expert white paper sets a European research agenda for combining quantum computing and AI, spanning quantum machine learning, AI-driven quantum control, and foundational questions.
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