arXiv AI

Minimal Decision Dynamics and Contextual Probability: A Quantum Tug-of-War Model

arXiv:2601. 10034v2 Announce Type: cross Abstract: Decision making often exhibits context dependence that challenges classical probability theory.

arXiv Machine Learning

Jul 21

Interpreting Quantum Learning Models via Stochastic Processes

arXiv:2607. 17327v1 Announce Type: cross Abstract: Quantum machine learning models define probabilistic input--output maps through coherent quantum evolution and measurement.

By Johannes Fankhauser, Lukas J. Fiderer, Hans J. Briegel

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 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 Machine Learning

Jun 24

Foundations of Practical Quantum Advantage in Quantum-Informed Machine Learning for Predicting Chaos

arXiv:2606. 13422v2 Announce Type: replace-cross Abstract: We develop theoretical foundations for a practical quantum-advantage mechanism in quantum-informed machine learning for chaotic dynamical systems.

By Maida Wang, Xiao Xue, Minh Chung, Peter V. Coveney

arXiv Machine Learning

Jun 19

QMaxCal: Path-Space Regularization for Open Quantum Control via Girsanov's Theorem

arXiv:2606. 19947v1 Announce Type: cross Abstract: Reliable quantum control in the presence of decoherence requires policies that combat the effect of environmental noise on the controlled dynamics.

By Merijn Moody, Zier Mensch, Miranda C. N. Cheng, Peter G. Bolhuis, Max Welling

benchmarks

arXiv Machine Learning

1d ago

Continuous Quantum Feedback Control via Kraus-Parameterized Belief Reinforcement Learning

arXiv:2608. 15715v1 Announce Type: cross Abstract: Quantum feedback control requires acting on noisy continuous measurement records without direct access to the underlying quantum state.

By Priyanshi Singh, Krishna Bhatia

rag reinforcement-learning safety

arXiv AI

Jul 2

When AI meets quantum information: A comprehensive review

arXiv:2607. 00365v1 Announce Type: cross Abstract: Artificial intelligence (AI) and quantum information (QI) are rapidly co-evolving.

By Min Chen, Yu Gan, Xin Jin, Yuqing Li, Junqi Wang, Zeguan Wu, Yunfei Wang, Bingzhi Zhang, Priyam Srivastava, Tianlong Chen, Ankit Kulshrestha, Yuan Liu, Juan Jos\'e Mendoza-Arenas, Kaushik P. Seshadreesan, Sarvagya Upadhyay, Xueyue Zhang, Quntao Zhuang, Junyu Liu

arXiv AI

Jul 20

A Bit of Freedom Goes a Long Way: Classical and Quantum Algorithms for Reinforcement Learning under a Generative Model

arXiv:2507. 22854v3 Announce Type: replace-cross Abstract: We propose novel classical and quantum online algorithms for learning finite- and infinite-horizon Markov Decision Processes (MDPs).

By Andris Ambainis, Joao F. Doriguello, Debbie Lim

agents reinforcement-learning

arXiv AI

Jun 4

Quantum entanglement provides a competitive advantage in adversarial games

arXiv:2603. 10289v2 Announce Type: replace-cross Abstract: Whether uniquely quantum resources confer advantages in fully classical, competitive environments remains an open question.

By Peiyong Wang, Kieran Hymas, James Quach

agents reinforcement-learning safety

arXiv Machine Learning

Jul 8

Entanglement as a Structural Complexity Axis: A PAC-Bayesian View of Generalization in Quantum Policies and Value Functions

arXiv:2607. 06230v1 Announce Type: cross Abstract: Parameterized quantum circuits (PQCs) are increasingly used as policies and value functions in quantum reinforcement learning, yet it remains unclear when and why quantum policies generalize.

By Jian Xu, Delu Zeng, John Paisley, Qibin Zhao

reinforcement-learning

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

reinforcement-learning

arXiv Machine Learning

Aug 6

Leveraging Machine Learning to Gain Insights on Quantum Thermodynamic Entropy

arXiv:2305. 06177v1 Announce Type: cross Abstract: We present a thermodynamic analysis of a quantum engine that uses a single quantum particle as its working fluid, inspired by Szilard's classical single-particle engine.

By Srinivasa Rao. P

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