arXiv AI August 3, 2026 By Jiayang Niu, Yan Wang, Jie Li, Ke Deng, Azadeh Alavi, Muhammad Usman, Yongli Ren

DreamQAS: Learning a Decision-Useful World Model for VQE-Efficient Quantum Architecture Search

arXiv:2607. 29491v1 Announce Type: cross Abstract: Reinforcement-learning-based quantum architecture search (RL-QAS) repeatedly optimizes a variational quantum eigensolver (VQE) after extending a circuit, although circuit construction and action legality are deterministic and known.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

reinforcement-learning

arXiv Machine Learning

Jun 18

Quantum Annealing Enhanced Reinforcement Learning for Accurate Remaining Useful Lifetime Prediction

arXiv:2606. 18503v1 Announce Type: new Abstract: Remaining useful life (RUL) estimation is central to predictive maintenance, where an unplanned failure can cost far more than the asset itself.

By Manoranjan Gandhudi, Arunkumar V., G. R. Anil, Gangadharan G. R

rag reinforcement-learning benchmarks

arXiv Machine Learning

Jul 1

Quantum Bayesian Networks Can Speed up Reinforcement Learning in Partially Observable Environments

arXiv:2507. 18606v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) provides a principled framework for decision-making in partially observable environments, which can be modeled as Markov decision processes and compactly represented through dynamic decision Bayesian networks.

By Gilberto Cunha, Alexandra Ram\^oa, Andr\'e Sequeira, Michael de Oliveira, Lu\'is Barbosa

reinforcement-learning benchmarks

arXiv AI

Jul 23

Hybrid LLM-Guided Search for Quantum Reservoir Architecture Design

arXiv:2607. 19506v1 Announce Type: cross Abstract: Quantum reservoir computing (QRC) uses fixed quantum dynamics as a high-dimensional temporal feature map and trains only a lightweight classical readout.

By Krishna Bhatia, Gautami Sanjay Naik

llms agents benchmarks

arXiv Machine Learning

Jun 9

QnRL: Quantum-Native Reinforcement Learning

arXiv:2606. 08276v1 Announce Type: cross Abstract: Quantum reinforcement learning (QRL) is a promising approach to learn effective decision strategies across several applications with stochastic environments.

By Alexander DeRieux, Walid Saad

reinforcement-learning

arXiv AI

Jul 22

Robust Belief-State Policy Learning for Quantum Network Routing Under Decoherence and Time-Varying Conditions

arXiv:2509. 08654v2 Announce Type: replace-cross Abstract: Quantum network routing requires online decisions under probabilistic entanglement generation, finite quantum memories, decoherence, imperfect operations, and classical feedback, while the controller has incomplete knowledge of the physical state.

By Amirhossein Taherpour, Abbas Taherpour, Tamer Khattab, Mazen Hasna

reinforcement-learning

arXiv Machine Learning

Jul 24

Approximate Quantum State Preparation Through Proximal Policy Optimization

arXiv:2607. 21121v1 Announce Type: cross Abstract: In this work, a quantum architecture search framework for approximate quantum state preparation (QSP) is proposed.

By Marco Mordacci, Michele Amoretti

agents reinforcement-learning

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Quantum Annealing Enhanced Reinforcement Learning for Accurate Remaining Useful Lifetime Prediction

Quantum Bayesian Networks Can Speed up Reinforcement Learning in Partially Observable Environments

Hybrid LLM-Guided Search for Quantum Reservoir Architecture Design

QnRL: Quantum-Native Reinforcement Learning

Robust Belief-State Policy Learning for Quantum Network Routing Under Decoherence and Time-Varying Conditions

Approximate Quantum State Preparation Through Proximal Policy Optimization