Junseo Lee
About Me
Hello! I’m Junseo Lee, also known as Harris.
I received my bachelor’s degree in Electrical and Electronic Engineering from Yonsei University, fully supported by the Hyundai Motor Chung Mong-Koo Scholarship. I am currently fulfilling my mandatory national service through an alternative program, working as a research scientist in theoretical quantum computing.
Since my undergraduate years, I have been part of the Quantum Information Theory Group at the Research Institute of Mathematics, Seoul National University, initially as a research assistant and now as a research associate, continuing my work with the group. For more details, you can find my CV here.
Contact: I love discussing new research ideas and beyond, so feel free to reach out to me at harris.junseo(at)gmail.com for collaborations, questions, or just to connect. I’m always happy to hear from you!
You can also find me on LinkedIn and Google Scholar.
Research Interests
My main research interest lies in the theory of quantum computation, with a particular curiosity about how physical and computational resources and constraints shape the behavior of quantum systems. More broadly, I am also interested in theoretical computer science and quantum information.
Recently, my primary research focuses on the following topics:
Quantum Learning Theory
I study efficient algorithms and lower bounds for learning and testing tasks on both discrete and continuous-variable quantum systems, including quantum states, unitaries, and channels. A central question I pursue is "how different resources affect the feasibility and complexity of extracting information from quantum systems."
Quantum Complexity Theory
I investigate the capabilities and limitations of quantum proof systems and complexity classes, with particular attention to "how restrictions such as quantum advice, entanglement, or uniqueness conditions change the computational power of quantum models." This includes studying quantum-classical separations and structural properties of quantum complexity classes.
Quantum Algorithms
For me, learning theory represents the “final step” of quantum information processing, while complexity theory provides the fundamental landscape of what quantum computers can or cannot do. Building on these theoretical foundations, I aim to design efficient quantum algorithms and protocols that leverage quantum advantages in solving concrete problems, particularly in algebra, combinatorics, and topology.
I have also worked on other topics in the past, including quantum channel capacity and the quantum entropy power inequality, and quantum machine learning and its applications.