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Cited by 28Quantum Computing Algorithms and ArchitectureReinforcement Learning in RoboticsQuantum Information and Cryptography

QTRL: Toward Practical Quantum Reinforcement Learning via Quantum-Train

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Liu, Chen-Yu, Lin, Chu-Hsuan Abraham, Yang, Chao-Han Huck, Chen, Kuan-Cheng, Hsieh, Min-Hsiu·2024·2024 IEEE International Conference on Quantum Computing and Engineering (QCE)
DOI 10.1109/qce60285.2024.10299
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Abstract

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.

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  • The QT framework has demonstrated effectiveness in image classification on MNIST and CIFAR-10, reinforcement learning, time-series forecasting for flood prediction, and federated learning.

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    From Quantum-Trained Convolutional Neural Network for Deepfake Audio Detection (2025) · cites this paper as [23]

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Cited by

Sorted by citations

Authors
Liu, C.-Y., Lin, C.-H. A., Yang, C.-H. H., Chen, K.-C., & Hsieh, M.-H.
Affiliations
  • Foxconn (Taiwan) (TW)
  • Imperial College London (GB)
  • NVIDIA,Taipei,Taiwan
ORCID
Venue
2024 IEEE International Conference on Quantum Computing and Engineering (QCE)
Publisher
IEEE
Published
2024-09-15
Pages
317-322
DOI
10.1109/qce60285.2024.10299
OpenAlex
W4406261916
Cited by
28
References
6
Citations by year
2026: 2 · 2025: 21 · 2024: 5
FWCI
8.08
Type
conference paper
Language
en
Open access
Licence
Locations
  • IEEE International Conference on Quantum Computing and Engineering (QCE) (publishedVersion)
  • arXiv (Cornell University) (submittedVersion, cc-by-nc-nd)
  • arXiv (Cornell University)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
  • Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
  • Physical Sciences › Computer Science › Artificial Intelligence › Quantum Information and Cryptography
Keywords
quantum reinforcement learning, hybrid quantum-classical models, data encoding, parameter reduction, classical inference, quantum machine learning

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