Hailan Ma
Portrait of Hailan Ma

"Learning to Represent, decisoin-make, and Deploy"

Eric and Wendy Schmidt AI in Science Postdoctoral Fellow

Hailan Ma

Nanyang Quantum Hub, Nanyang Technological University, Singapore

I am Hailan Ma, currently a Schmidt AI in Science Postdoctoral Fellow at Nanyang Technological University. I received my PhD in Electrical Engineering from the University of New South Wales. My research explores machine learning for quantum systems. Broadly speaking, my work evolves along two directions: AI for Quantum, where I develop neural representations and reinforcement learning for quantum estimation, tomography, and compression; and more recently Quantum-inspired AI, which explores how quantum principles can inspire next-generation AI systems.

Scholar ORCID LinkedIn CV

Research Focus

AI Meets Quantum to Science

  • Representation makes quantum systems learnable: Compact quantum representation and reduced-order modeling make efficient reconstruction of quantum states and processes from limited measurements, reducing experimental complexity and improving the learnability of quantum systems.
  • Decison enable quantum systems controllable: Reinforcement learning control and adaptive algorithms enable data-driven optimization of quantum dynamics under uncertainty, realizing robust autonomous control of complex quantum systems.
  • Deployment renders quantum systems scalable: Robust quantum algorithms and scalable experiments redener resource-efficient implementation of quantum information processing, supporting practical deployment on real-world quantum hardware.

Quantum-Inspired Intelligent Agent

  • Decision → Learning-efficient intelligent agents: Quantum reinforcement learning and quantum-aware policy models enable improved exploration, structured sample reuse, and adaptive decision-making under partial observability.
  • Representation → Expressive AI systems: Quantum-inspired representation learning and quantum latent compression enable structured, compact neural representations that improve memory efficiency and modeling of uncertainty.

News

  • 2026 Serving as the first organizer of the 2026 IEEE Systems, Man, and Cybernetics Society Summer School on Quantum Cybernetics and AI.
  • 2025 Started as the Eric and Wendy Schmidt AI in Science Postdoctoral Fellow at the Nanyang Quantum Hub, NTU Singapore.
  • 2025 Served as host and organizer of the qCCL 2025 Pre-Conference Workshop for Quantum Estimation and Control in Hong Kong.
  • 2024 Received the Dean's Award for Outstanding PhD Thesis from UNSW.
  • 2024 Received the Ria De Groot Prize for best performance by a female postgraduate from UNSW.
  • 2024 Joined seminars and academic visits at NTU, the University of Melbourne, and the Academy of Mathematics and Systems Science, CAS.

Representative Publications

View all publications
  1. Machine Learning for Estimation and Control of Quantum Systems
    Hailan Ma, B. Qi, I. R. Petersen, R.-B. Wu, H. Rabitz, D. Dong
    National Science Review, 2025. Link
    This paper addresses the lack of a unified view of learning-based quantum estimation and control, and helps clarify the field's main opportunities and practical impact.
  2. Quantum autoencoders using mixed reference states
    Hailan Ma, G. J. Mooney, I. R. Petersen, L. L. Hollenberg, D. Dong
    npj Quantum Information, 2024. Link
    This paper fills a practical gap in quantum autoencoding by extending the framework beyond idealized pure-reference settings to more realistic mixed states.
  3. Tomography of Quantum States from Structured Measurements via quantum-aware transformer
    Hailan Ma, Z. Sun, D. Dong, C. Chen, H. Rabitz
    IEEE Transactions on Cybernetics, 2025. Link
    It tackles the gap between transformer models and structured quantum measurements, showing improved state reconstruction in constrained settings.
  4. Curriculum-based deep reinforcement learning for quantum control
    Hailan Ma, D. Dong, S. X. Ding, C. Chen
    IEEE Transactions on Neural Networks and Learning Systems, 2023. Link
    It addresses unstable learning in difficult quantum-control tasks and shows how curriculum design can improve convergence and performance.
  5. On compression rate of quantum autoencoders: Control design, numerical and experimental realization
    Hailan Ma, C.-J. Huang, C. Chen, D. Dong, Y. Wang, R.-B. Wu, G.-Y. Xiang
    Automatica, 2023. Link
    It fills a gap between theory and experiment in quantum compression by combining control design, numerical study, and experimental realization.

Contact

Email hailan.ma@ntu.edu.sg
Affiliation Nanyang Quantum Hub, Nanyang Technological University
Location Singapore