Quantum Reinforcement Learning by Adaptive Non-local Observables
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916864883425280 |
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| author | Lin, Hsin-Yi Chen, Samuel Yen-Chi Tseng, Huan-Hsin Yoo, Shinjae |
| author_facet | Lin, Hsin-Yi Chen, Samuel Yen-Chi Tseng, Huan-Hsin Yoo, Shinjae |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_19629 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quantum Reinforcement Learning by Adaptive Non-local Observables Lin, Hsin-Yi Chen, Samuel Yen-Chi Tseng, Huan-Hsin Yoo, Shinjae Quantum Physics Artificial Intelligence Machine Learning 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. |
| title | Quantum Reinforcement Learning by Adaptive Non-local Observables |
| topic | Quantum Physics Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2507.19629 |