Quantum Reinforcement Learning by Adaptive Non-local Observables

• URL: http://arxiv.org/abs/2507.19629v1
• Date: Fri, 25 Jul 2025 18:57:16 GMT
• Title: Quantum Reinforcement Learning by Adaptive Non-local Observables
• Authors: Hsin-Yi Lin, Samuel Yen-Chi Chen, Huan-Hsin Tseng, Shinjae Yoo,
• Abstract summary: We introduce an adaptive non-local observable (ANO) paradigm within variational quantum circuits (VQCs)<n>ANO-VQC architecture serves as the function approximator in Deep Q-Network (DQN) and Asynchronous Advantage Actor-Critic (A3C) algorithms.<n>Our results demonstrate that adaptive multi-qubit observables can enable practical quantum advantages in reinforcement learning.
• Score: 10.617463958884528
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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