{"ID":2890635,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.19629","arxiv_id":"2507.19629","title":"Quantum Reinforcement Learning by Adaptive Non-local Observables","abstract":"Hybrid quantum-classical frameworks leverage quantum computing for machine learning; however, variational quantum circuits (VQCs) are limited by the need for local measurements. We introduce an adaptive non-local observable (ANO) paradigm within VQCs for quantum reinforcement learning (QRL), jointly optimizing circuit parameters and multi-qubit measurements. The ANO-VQC architecture serves as the function approximator in Deep Q-Network (DQN) and Asynchronous Advantage Actor-Critic (A3C) algorithms. On multiple benchmark tasks, ANO-VQC agents outperform baseline VQCs. Ablation studies reveal that adaptive measurements enhance the function space without increasing circuit depth. Our results demonstrate that adaptive multi-qubit observables can enable practical quantum advantages in reinforcement learning.","short_abstract":"Hybrid quantum-classical frameworks leverage quantum computing for machine learning; however, variational quantum circuits (VQCs) are limited by the need for local measurements. We introduce an adaptive non-local observable (ANO) paradigm within VQCs for quantum reinforcement learning (QRL), jointly optimizing circuit...","url_abs":"https://arxiv.org/abs/2507.19629","url_pdf":"https://arxiv.org/pdf/2507.19629v1","authors":"[\"Hsin-Yi Lin\",\"Samuel Yen-Chi Chen\",\"Huan-Hsin Tseng\",\"Shinjae Yoo\"]","published":"2025-07-25T18:57:16Z","proceeding":"quant-ph","tasks":"[\"quant-ph\",\"cs.AI\",\"cs.LG\"]","methods":"[\"Reinforcement Learning\"]","has_code":false}