Harris Junseo Lee

Hello! My name is Junseo Lee (also known as Harris).

I am currently fulfilling my military alternative service as a Quantum Computing Researcher at Norma Inc. Additionally, I am working as a Research Assistant with Quantum Information Theory Group at the Research Institute of Mathematics, Seoul National University.

My main research areas are quantum information and quantum computation, with a primary focus on theoretical works. I enjoy exploring quantum Shannon theory, quantum learning theory, quantum complexity theory, and more recently, quantum error correction codes. For more information about my research, please refer to the list of my publications and presentations.

I am preparing to pursue a Ph.D. starting in Fall 2026, coinciding with the end of my military service.

For research collaborations or any other inquiries, please feel free to contact me at harris.junseo@gmail.com. You can also find me on LinkedIn and Google Scholar.

news

Jul 25, 2024	I gave an invited lecture at Seoul National University titled “QMA ≠ NP? The Remaining Journey in Quantum Complexity Theory: The NLTS Theorem and the Quantum PCP Conjecture.”
Jun 25, 2024	I will be participating in Quantum Korea, held at the Korea International Exhibition Center Company for three days starting from June 25.
Jun 19, 2024	I will be participating in the International Conference on Quantum Simulation from June 19 to 21, 2024, hosted by Ewha Womans University.
Jun 18, 2024	My new website has been launched!
Feb 28, 2024	I participated in the 2024 Annual Meeting of the Quantum Information Society of Korea, from April 21 to April 23, 2024, at the Busan Exhibition and Convention Center (BPEX).

selected publications

QML

Estimating quantum mutual information through a quantum neural network

Myeongjin Shin, Junseo Lee, and Kabgyun Jeong

Quantum Information Processing, 2024

Abs LINK

We propose a method of quantum machine learning called quantum mutual information neural estimation (QMINE) for estimating von Neumann entropy and quantum mutual information, which are fundamental properties in quantum information theory. The QMINE proposed here basically utilizes a technique of quantum neural networks (QNNs), to minimize a loss function that determines the von Neumann entropy, and thus quantum mutual information, which is believed more powerful to process quantum datasets than conventional neural networks due to quantum superposition and entanglement. To create a precise loss function, we propose a quantum Donsker-Varadhan representation (QDVR), which is a quantum analog of the classical Donsker-Varadhan representation. By exploiting a parameter shift rule on parameterized quantum circuits, we can efficiently implement and optimize the QNN and estimate the quantum entropies using the QMINE technique. Furthermore, numerical observations support our predictions of QDVR and demonstrate the good performance of QMINE.
QST

Quantum Rényi entropy functionals for bosonic gaussian systems

Junseo Lee, and Kabgyun Jeong

Physics Letters A. (Special Issue) Foundations and Applications of Quantum Optics , 2023

Abs LINK

In this study, the quantum Rényi entropy power inequality of order p>1 and power κ is introduced as a quantum analog of the classical Rényi-p entropy power inequality. To derive this inequality, we first exploit the Wehrl-p entropy power inequality on bosonic Gaussian systems via the mixing operation of quantum convolution, which is a generalized beam-splitter operation. This observation directly provides a quantum Rényi-p entropy power inequality over a quasi-probability distribution for D-mode bosonic Gaussian regimes. The proposed inequality is expected to be useful for the nontrivial computing of quantum channel capacities, particularly universal upper bounds on bosonic Gaussian quantum channels, and a Gaussian entanglement witness in the case of Gaussian amplifier via squeezing operations.