arXiv AI

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.

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
arXiv AI
Aug 3

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.

By Jiayang Niu, Yan Wang, Jie Li, Ke Deng, Azadeh Alavi, Muhammad Usman, Yongli Ren
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
Hugging Face Trending Papers
Jul 2

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

Neural quantum states (NQS) provide a flexible and scalable framework for approximating quantum many-body wavefunctions. Among NQS parameterizations, autoregressive models are especially attractive because they enable exact, independent sampling from the Born distribution, avoiding the autocorrelation and mixing issues of Markov chain methods.