QTRL: Toward Practical Quantum Reinforcement Learning via Quantum-Train
Fuente:
arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866914861873627136 |
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| author | Liu, Chen-Yu Lin, Chu-Hsuan Abraham Yang, Chao-Han Huck Chen, Kuan-Cheng Hsieh, Min-Hsiu |
| author_facet | Liu, Chen-Yu Lin, Chu-Hsuan Abraham Yang, Chao-Han Huck Chen, Kuan-Cheng Hsieh, Min-Hsiu |
| contents | Quantum reinforcement learning utilizes quantum layers to process information within a machine learning model. However, both pure and hybrid quantum reinforcement learning face challenges such as data encoding and the use of quantum computers during the inference stage. We apply the Quantum-Train method to reinforcement learning tasks, called QTRL, training the classical policy network model using a quantum machine learning model with polylogarithmic parameter reduction. This QTRL approach eliminates the data encoding issues of conventional quantum machine learning and reduces the training parameters of the corresponding classical policy network. Most importantly, the training result of the QTRL is a classical model, meaning the inference stage only requires classical computer. This is extremely practical and cost-efficient for reinforcement learning tasks, where low-latency feedback from the policy model is essential. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_06103 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | QTRL: Toward Practical Quantum Reinforcement Learning via Quantum-Train Liu, Chen-Yu Lin, Chu-Hsuan Abraham Yang, Chao-Han Huck Chen, Kuan-Cheng Hsieh, Min-Hsiu Quantum Physics Quantum reinforcement learning utilizes quantum layers to process information within a machine learning model. However, both pure and hybrid quantum reinforcement learning face challenges such as data encoding and the use of quantum computers during the inference stage. We apply the Quantum-Train method to reinforcement learning tasks, called QTRL, training the classical policy network model using a quantum machine learning model with polylogarithmic parameter reduction. This QTRL approach eliminates the data encoding issues of conventional quantum machine learning and reduces the training parameters of the corresponding classical policy network. Most importantly, the training result of the QTRL is a classical model, meaning the inference stage only requires classical computer. This is extremely practical and cost-efficient for reinforcement learning tasks, where low-latency feedback from the policy model is essential. |
| title | QTRL: Toward Practical Quantum Reinforcement Learning via Quantum-Train |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2407.06103 |