High-performance scientific and quantum computing
Research Field
My research lies at the intersection of HPC and theoretical physics. Beyond simulating hyperbolic PDEs in numerical relativity and astrophysics, I collaborate with users to parallelize and optimize codes to push computing capacities. Joining KAGRA in 2017, I began conducting gravitational wave detection and parameter estimation as multi-channel time-series analysis problems; these challenges offer significant applications in statistics and quantum computation. Since 2021, I have focused on quantum computing, specifically exploring quantum-classical hybrid frameworks and algorithms.
On the service side, I support the computing of two science projects: managing a CMS Tier-2 site for the WLCG in collaboration with Taiwan's CMS group since 2015 (under the CERN-Taiwan MoU) and contributing to the International Gravitational-Wave Network (IGWN) via the Open Science Grid (OSG) since 2023.
Ｗe are dedicated to the exploration of frontier computing technologies and their applications, leveraging the HPC resource at NCHC to bridge the gap between theoretical research and large-scale computational implementation.
- Quantum Computing: Characterization, benchmark, and application of quantum algorithms for optimization and quantum simulation tasks.
- Computational Physics: Application with stencil-based PDE simulations utilizing adaptive mesh refinement (AMR).
- HPC Characterization: Performance benchmark, optimization, and profiling.
None
- 2001/09 - 2009/07 PhD in Theoretical Physics, NCKU (Quantum Information & General Relativity)
- 1996/09 - 2001/07 BSc in Physics, NCKU
Job Description
- Investigate and implement quantum algorithms for tasks like optimization, quantum dynamics simulations, and quantum machine learning. These include, but are not limited to, annealing using the QUBO formulation and gate-based hybrid methods, such as QAOA.
- Running simulations on GPU clusters or small-scale quantum devices using NVIDIA’s CUDA-Q or IBM’s Qiskit frameworks
Preferred Intern Educational Level
Open to all levels.
Skill sets or Qualities
- Proficiency in Python or C/C++.
- Bonus: Familiarity with quantum computing/mechanics is a plus.
Job Description
- Investigate GPU parallel programming frameworks such as KOKKOS or AMReX.
- Improve the MPI/OpenACC-based 3D finite difference/element simulation code (for the collective neutrino simulation) with the native CUDA or advanced frameworks.
Preferred Intern Educational Level
Open to all levels.
Skill sets or Qualities
- Proficiency in C/C++ programming.
- Experience with the finite difference method for solving PDEs.