Chen-Yu Liu

Currently a Research Scientist at Quantinuum London, and a Ph.D. candidate at the Graduate Institute of Applied Physics, National Taiwan University.

Chen-Yu Liu is a Research Scientist at Quantinuum London and a Ph.D. candidate in Applied Physics at National Taiwan University, advised by Prof. Hsi-Sheng Goan. He specializes in quantum computing, quantum machine learning, and parameter-efficient learning techniques. His research focuses on developing hybrid quantum-classical algorithms such as Quantum Parameter Adaptation (QPA) and Quantum-Train (QT), which leverage quantum computing to enhance neural network training and model compression. He has published his work at leading AI and quantum computing conferences, including ICLR, ICASSP, IJCNN, QCE, ICTC, and QCNC, contributing to advancements in quantum-enhanced AI models and efficient learning strategies. Previously, he interned at Hon Hai (Foxconn) Research Institute, Jij, and Entropica Labs, where he explored practical quantum algorithms for deep learning and optimization. His long-term goal is to bridge the gap between quantum and classical machine learning, enabling scalable and resource-efficient quantum AI solutions.

Background

  • PhD in Applied Physics, National Taiwan University (2021-current)
  • MS in Physics, National Tsing Hua University (2018-2020)
  • BS in Physics, National Dong Hwa University (2014-2018)

Experience

ML Research Scientist, Quantum for AI

Quantinuum (2025-Current)

Research Intern

Quantum Computing Research Center, Hon Hai Research Institute (2022-2025)

Apprentice Quantum Researcher

Entropica Labs (2021)

  • Graph information reinforcement learning QAOA project
    • Propose a Graph info. Reinforcement Learning (GiRL) model that can solve QAOA problems. Such model can be trained in smaller Hamiltonian instances and shallower QAOA layers and apply to larger instances and deeper QAOA layers. The performance of approximate ratio is similar to Nelder-Mead(NM) with much less number of function evaluations (circuit executions) than NM.

Research Assistant

National Center for Theoretical Science (2020-2021)

  • Machine learning application in quantum many body system
    • Extend the study in master program and preparing a paper for journal publication.

Master Program

National Tsing Hua University, Department of Physics (2018-2020)

  • Machine learning application in quantum many body system:
    • Propose a new approach to predict Hamiltonian-dependent physical quantities such as eigenvalues and dispersion relation in non-perturbative parameter regime from perturbative regime data whose computational time remain almost constantly with respect to increasing system size. The accuracy of prediction can be affected by several parameters. The potential of this method theoretically should including any calculation of Hamiltonian-dependent physical quantities especially in an extremely large Hilbert space system.
  • Teaching assistant of Quantum Mechanics (I), (II) (2019-2020)

Software development internship

Wistron Neweb Corporation, Automotive & Industrial Solutions Business Group (2019)

  • Developed a tool for testing purposes by C++, python, Linux shell. Migrate codes from customers to match company’s requirements

Research Internship

Academia Sinica, Institute of Atomic and Molecular Science (2018)

  • Effective quantum theory of molecular system research: Examine the behavior of an anharmonic coupling model in the strong coupling limit and construct a much simpler effective model.

College Student Research Scholarship

National Science Council (2017-2018)

  • Geodesic motion of particle around a Kerr-Newman black hole:
    • Examine the dynamics of a particle around Kerr–Newman black hole. Analytically and numerically determine the parameter regions of the corresponding motions, in terms of the initial radius of the orbital motion and the strength of the perturbation. The comparison has been made with the motion of a neutral particle in the Kerr black hole.