Hsieh, Min-Hsiu

Expressive power of parametrized quantum circuits

Du, Y., Hsieh, M.-H., Liu, T., & Tao, D. (2020) · Physical Review Research

Quantum Computing Algorithms and ArchitectureQuantum many-body systems

Cited by 318

Entanglement-Assisted Communication of Classical and Quantum Information

Hsieh, M.-H., & Wilde, M. M. (2010) · IEEE Transactions on Information Theory

Quantum Information and CryptographyQuantum Computing Algorithms and Architecture

Cited by 112

Entanglement-Assisted Quantum Turbo Codes

Wilde, M. M., Hsieh, M.-H., & Babar, Z. (2014) · IEEE Transactions on Information Theory

Quantum Computing Algorithms and ArchitectureQuantum Information and Cryptography

Cited by 102

NP-hardness of decoding quantum error-correction codes

Hsieh, M.-H., & Le Gall, F. (2011) · Physical Review A

Quantum Computing Algorithms and ArchitectureCoding theory and cryptography

Cited by 71

Trading classical communication, quantum communication, and entanglement in quantum Shannon theory

Hsieh, M.-H., & Wilde, M. M. (2010) · IEEE Transactions on Information Theory

Quantum Information and CryptographyQuantum Computing Algorithms and Architecture

Cited by 68

Quantum-train: Rethinking hybrid quantum-classical machine learning in the model compression perspective

Liu, C.-Y., Kuo, E.-J., Abraham Lin, C.-H., Gemsun Young, J., Chang, Y.-J., Hsieh, M.-H., & Goan, H.-S. (2025) · Quantum Machine Intelligence

Quantum Computing Algorithms and ArchitectureQuantum many-body systems

Cited by 40

Toward Trainability of Quantum Neural Networks

Zhang, K., Hsieh, M.-H., Liu, L., & Tao, D. (2020) · arXiv

Quantum Computing Algorithms and ArchitectureQuantum many-body systems

Cited by 36

QTRL: Toward Practical Quantum Reinforcement Learning via Quantum-Train

Liu, C.-Y., Lin, C.-H. A., Yang, C.-H. H., Chen, K.-C., & Hsieh, M.-H. (2024) · 2024 IEEE International Conference on Quantum Computing and Engineering (QCE)

Quantum Computing Algorithms and ArchitectureReinforcement Learning in Robotics

Cited by 28