Zobrazeno 1 - 10
of 115
pro vyhledávání: '"Lee, Junseo"'
Autor:
Lee, Junseong
Cardiovascular disease is a major cause of mortality in the world, and is rapidly increasing. To understand the inflammatory response of various diseases, we have made tremendous advances in molecular and cellular research to show clear evidence that
Training agents that are robust to environmental changes remains a significant challenge in deep reinforcement learning (RL). Unsupervised environment design (UED) has recently emerged to address this issue by generating a set of training environment
Externí odkaz:
http://arxiv.org/abs/2410.19715
Estimating the trace of powers of identical $k$ density matrices (i.e., $\text{Tr}(\rho^k)$) is a crucial subroutine for many applications such as calculating nonlinear functions of quantum states, preparing quantum Gibbs states, and mitigating quant
Externí odkaz:
http://arxiv.org/abs/2408.00314
In the realm of autonomous agents, ensuring safety and reliability in complex and dynamic environments remains a paramount challenge. Safe reinforcement learning addresses these concerns by introducing safety constraints, but still faces challenges i
Externí odkaz:
http://arxiv.org/abs/2407.02245
Autor:
Shin, Myeongjin, Lee, Seungwoo, Lee, Junseo, Lee, Mingyu, Ji, Donghwa, Yeo, Hyeonjun, Jeong, Kabgyun
The estimation of quantum entropies and distance measures, such as von Neumann entropy, R\'enyi entropy, Tsallis entropy, trace distance, and fidelity-induced distances like Bures distance, has been a key area of research. This paper introduces a uni
Externí odkaz:
http://arxiv.org/abs/2401.07716
We study the $p$-R\'{e}nyi entropy power inequality with a weight factor $t$ on two independent continuous random variables $X$ and $Y$. The extension essentially relies on a modulation on the sharp Young's inequality due to Bobkov and Marsiglietti.
Externí odkaz:
http://arxiv.org/abs/2311.06484
Mutual Information Maximizing Quantum Generative Adversarial Network and Its Applications in Finance
One of the most promising applications in the era of NISQ (Noisy Intermediate-Scale Quantum) computing is quantum machine learning. Quantum machine learning offers significant quantum advantages over classical machine learning across various domains.
Externí odkaz:
http://arxiv.org/abs/2309.01363
Publikováno v:
Quantum Information Processing 23, 57 (2024)
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 p
Externí odkaz:
http://arxiv.org/abs/2306.14566
Autor:
Lee, Junseo
With the advancement of quantum technologies, there is a potential threat to traditional encryption systems based on integer factorization. Therefore, developing techniques for accurately measuring the performance of associated quantum algorithms is
Externí odkaz:
http://arxiv.org/abs/2305.05249
Autor:
Lee, Junseo, Jeong, Kabgyun
Publikováno v:
Phys. Lett. A 490, 129183 (2023)
In this study, the quantum R\'{e}nyi entropy power inequality of order $p>1$ and power $\kappa$ is introduced as a quantum analog of the classical R\'{e}nyi-$p$ entropy power inequality. To derive this inequality, we first exploit the Wehrl-$p$ entro
Externí odkaz:
http://arxiv.org/abs/2204.10737