Quantum-train: Rethinking hybrid quantum-classical machine learning in the model compression perspective
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The QT framework reduces QNN parameters to O(polylog(M)) compared to M for classical NNs.
“scaling down from M parameters in classical NNs to O ( polylog ( M )) by using QNNs [21].”
From Quantum-Trained Convolutional Neural Network for Deepfake Audio Detection (2025) · cites this paper as [21]
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.
“QT has demonstrated its versatility and effectiveness in various applications, including image classification on datasets like MNIST and CIFAR-10 [21], [25], reinforcement learning [23], and time-series forecasting for flood prediction [24]. It further shows potential in federated learning by compressing models and maintaining performance while reducing computational overhead [22].”
From Quantum-Trained Convolutional Neural Network for Deepfake Audio Detection (2025) · cites this paper as [21]
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Hsu, Y.-C., Jiang, J.-C., Lin, C.-H., Chen, W.-T., Peng, K. C., Tiwari, P., Chen, S. Y.-C., & Kuo, E.-J. (2025) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce65121.2025.10293 · Cited by 3 · see citing works
Self-citationA hybrid QSVM-QNN approach for breast cancer diagnosis using quantum feature maps
Yadav, V., Sheoran, S. K., & Sheoran, R. K. (2026) · PeerJ Computer Science · doi:10.7717/peerj-cs.3600 · Cited by 4 · see citing works
Distributed Quantum Neural Networks on Distributed Photonic Quantum Computing
Chen, K., Liu, C.-Y., Shang, Y., Burt, F., & Leung, K. K. (2025) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce65121.2025.00165 · Cited by 3 · see citing works
Self-citationSun, Q., Chen, J., Fan, Y., Kong, X., Jiang, H., Li, L., Wang, M., Xuan, A., & Yang, X. (2026) · Scientific Reports · doi:10.1038/s41598-026-40127-z · Cited by 2 · see citing works
Unraveling quantum environments: Transformer-assisted learning in Lindblad dynamics
Chen, C., & Kuo, E.-J. (2025) · Physical Review A · doi:10.1103/gsxk-45mk · Cited by 1 · see citing works
Self-citationLearning to Program Quantum Measurements for Machine Learning
Chen, S. Y.-C., Tseng, H.-H., Lin, H.-Y., & Yoo, S. (2025) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce65121.2025.00200 · Cited by 1 · see citing works
Limit shapes and large deviations in classical and quantum neural networks
Rahmati, M. R., Arce, F., & Gómez‐Flores, W. (2025) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2025.131181 · Cited by 1 · see citing works
QLGAN: a quantum-lineage graph attention network for temporal knowledge graph entity alignment
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Chen, S. Y.-C. (2026) · IEEE Circuits and Systems Magazine · doi:10.1109/mcas.2026.3660997 · Cited by 2 · see citing works
QKAN-LSTM: Quantum-Inspired Kolmogorov–Arnold Long Short-Term Memory
Hsu, Y.-C., Jiang, J.-C., Lin, C.-H., Peng, K. C., Chen, N.-Y., Chen, S. Y.-C., Kuo, E.-J., & Goan, H. (2026) · International Conference on Quantum Communications, Networking, and Computing (QCNC) · doi:10.1109/qcnc69040.2026.00107 · Cited by 1 · see citing works
Self-citationQuantum Feature Optimization for Enhanced Clustering of Blockchain Transaction Data
Tsai, Y., & Chen, S. Y.-C. (2025) · International Conference on Computer Communications and Networks (ICCCN) · doi:10.1109/icccn65249.2025.11133860 · Cited by 2 · see citing works
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Generative Modeling of Measurement Data for Quantum Parameter Adaptation
Liu, C.-Y., Chen, S. Y.-C., Chen, K., Huang, W., Huang, W.-H., & Chang, Y.-J. (2025) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce65121.2025.00187
Self-citationQuantum-Aware Large Language Models: Taxonomy, Architectures, and Future Applications
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The Qubit Shift: Unveiling Next-Generation Deep Learning with Quantum Computing
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Optimized Quantum-Enhanced Neural Network Architectures
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TNO-QA: Tensor-Network Optimized Quantum Attention
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HyQNet: A Hybrid Quantum–Classical Framework for Quantum Machine Learning Optimization
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- Authors
- Liu, C.-Y., Kuo, E.-J., Abraham Lin, C.-H., Gemsun Young, J., Chang, Y.-J., Hsieh, M.-H., & Goan, H.-S.
- Affiliations
- National Taiwan University (TW)
- National Yang Ming Chiao Tung University (TW)
- Industrial Technology Research Institute (TW)
- Foxconn (Taiwan) (TW)
- Corresponding author
- Liu, Chen-Yu
- Goan, Hsi-Sheng
- Venue
- Quantum Machine Intelligence
- Published
- 2025-08-05
- Volume
- 7
- Issue
- 2
- ISSN
- 2524-4906 (ISSN-L)
- 2524-4914
- OpenAlex
- W4412956029
- Cited by
- 40
- References
- 76
- Citations by year
- 2026: 19 · 2025: 17 · 2024: 4
- FWCI
- 14.49
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Quantum Machine Intelligence (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by-nc-nd)
- arXiv (Cornell University)
- Funders
- National Science and Technology Council (NSTC 113-2119-M-002 -021)
- Air Force Office of Scientific Research (FA2386-20-1-4052)
- National Taiwan University (NTU-CC-112L893404)
- National Center for Theoretical Sciences (112-2124-M-002-003)
- Industrial Technology Research Institute (EOSL 2022 project Quantum Circuit Design and Application)
- Center for Advanced Computing and Imaging in Biomedicine (NTU-113L900702)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum many-body systems
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Information and Cryptography
- Keywords
- hybrid quantum-classical machine learning, quantum neural networks, model compression, parameter efficiency, LoRa, barren plateaus, quantum resource efficiency, quantum information encoding, noise robustness
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