arXiv AI

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.

arXiv Machine Learning

Jun 24

Reinforcement Learning to Disentangle Multiqubit Quantum States from Partial Observations

arXiv:2406. 07884v3 Announce Type: replace-cross Abstract: Using partial knowledge of a quantum state to control multiqubit entanglement is a largely unexplored paradigm in the emerging field of quantum interactive dynamics with the potential to address outstanding challenges in quantum state preparation and compression, quantum control, and quantum complexity.

By Pavel Tashev, Stefan Petrov, Matthew T. Diaz, Friederike Metz, Alaina M. Green, Norbert M. Linke, Marin Bukov

llms agents reinforcement-learning benchmarks

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

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 Machine Learning

23h ago

Dynamic Entanglement-Weighted Pruning for Quantum Federated Unlearning in Supply-Chain Risk Prediction

arXiv:2608. 17069v1 Announce Type: cross Abstract: Federated deployments of variational quantum classifiers are attractive for cross-organisation risk prediction in supply chains, because raw data never leaves the client, yet data-protection regulations such as the GDPR grant clients a right to request that their contribution be removed from a trained model after the fact.

By Aditya Kumar, Sumit Chongder

fine-tuning efficiency benchmarks

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

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 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 13

Action-Factored Multi-Agent Reinforcement Learning for Scalable Quantum Device Tuning

arXiv:2607. 09422v1 Announce Type: new Abstract: Cooperative multi-agent reinforcement learning is well suited to problems with large parameter spaces and exploitable local structure, such as the tuning of electrostatically-defined quantum-dot arrays.

By Edwin De Nicolo, Rahul Marchand, Cornelius Carlsson, Pranav Vaidhyanathan, Natalia Ares

agents reinforcement-learning

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 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

arXiv Machine Learning

Jul 30

Shot-based quantum encoding: a data-loading paradigm for quantum neural networks

arXiv:2604. 06135v2 Announce Type: replace-cross Abstract: Efficient data loading remains a bottleneck for near-term quantum machine learning.

By Basil Kyriacou, Viktoria Patapovich, Maniraman Periyasamy, Alexey Melnikov

rag benchmarks

arXiv Machine Learning

Jun 30

Staged Hybridisation for Visual Quantum Reinforcement Learning via Knowledge Distillation

arXiv:2606. 30520v1 Announce Type: cross Abstract: Visual environments are a demanding setting for quantum reinforcement learning (QRL): high-dimensional observations, unstable RL optimisation, and constrained variational quantum circuits (VQCs) are difficult to train jointly.

By Javier Lazaro, Juan-Ignacio Vazquez, Pablo Garcia-Bringas

agents reinforcement-learning efficiency

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