arXiv AI

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.

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

arXiv AI

Jun 9

Who Earns the Safety? Intervention-Aware Quantum Predictive Control with Safety Attribution

arXiv:2606. 09778v1 Announce Type: cross Abstract: Hard safety filters are increasingly placed downstream of learned controllers to guarantee constraint satisfaction at run time.

By Yifan Wang

safety

arXiv AI

Jun 10

Mitigating Bias in Low-SNR Financial Reinforcement Learning via Quantum Representations

arXiv:2606. 10448v1 Announce Type: cross Abstract: The financial market is a typical low signal-to-noise ratio (SNR) setting, which often destabilizes off-policy maximum-entropy methods like Soft Actor-Critic (SAC).

By Zeyu Liu, Xuanzhi Feng, Sing Kwong Lai, Yuanchen Gao, Xiaoyi Pang, Hualei Zhang, Jingcai Guo, Jie Zhang, Song Guo

reinforcement-learning safety

arXiv AI

Jun 16

Gated QKAN-FWP: Scalable Quantum-inspired Sequence Learning

arXiv:2605. 06734v2 Announce Type: replace-cross Abstract: Fast Weight Programmers (FWPs) encode temporal dependencies through dynamically updated parameters rather than recurrent hidden states.

By Kuo-Chung Peng, Samuel Yen-Chi Chen, Jiun-Cheng Jiang, Chen-Yu Liu, En-Jui Kuo, Yun-Yuan Wang, Prayag Tiwari, Andrea Ceschini, Chi-Sheng Chen, Yu-Chao Hsu, Chun-Hua Lin, Tai-Yue Li, Antonello Rosato, Massimo Panella, Simon See, Saif Al-Kuwari, Kuan-Cheng Chen, Nan-Yow Chen, Hsi-Sheng Goan

reinforcement-learning benchmarks

Hugging Face Trending Papers

Jun 11

SymQNet: Amortized Acquisition for Low-Latency Adaptive Hamiltonian Learning

Adaptive Hamiltonian learning is central to calibrating and characterizing quantum devices. In an adaptive controller, choosing the next experiment is itself a computation.

benchmarks

arXiv AI

Jun 12

SymQNet: Amortized Acquisition for Low-Latency Adaptive Hamiltonian Learning

arXiv:2606. 12808v1 Announce Type: cross Abstract: Adaptive Hamiltonian learning is central to calibrating and characterizing quantum devices.

By Yash Vardhan Tomar, Dheeraj Peddireddy, Vaneet Aggarwal

benchmarks

arXiv Machine Learning

Jul 3

One More Time: Revisiting Neural Quantum States from a Reinforcement Learning Perspective

arXiv:2607. 02292v1 Announce Type: new Abstract: Neural quantum states (NQS) provide a flexible and scalable framework for approximating quantum many-body wavefunctions.

By Juan Agust\'in Duque, Sergio Garc\'ia Heredia, Vinicius Hernandes, Eli\v{s}ka Greplov\'a, Thomas Spriggs, Aaron Courville, Anna Dawid

reinforcement-learning fine-tuning

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