From the 1 of 14 linked papers with an AI index.
14 papers
Hayden--Preskill recovery at finite temperature on a quantum processor: dynamics and initial state from the SYK model
Jeongho Bang, Moongul Byun, Kyoungho Cho +2
The paper extends the Hayden‑Preskill information recovery protocol by linking the scrambler to the initial state with a SWAP gate and studying recovery at finite temperature, show…
Hidden Complex Structure in Quotient-Space Real Quantum Mechanics
Jeongho Bang, Kyoungho Cho, Kyunghyun Baek
Barrios Hita et al. [Phys. Rev. Lett. , 240202 (2026)] argued that quantum mechanics can be formulated over the real numbers by replacing the tensor-product postulate wit…
Breaking the One-Dimensional Expressibility-Trainability Tradeoff
Kyoungho Cho, Yu-Seong Jeon, Jinhyoung Lee +1
Expressive parameterized quantum circuits (PQCs) are often designed under a dilemma: the growth of expressibility and entangling power (EP) that improves Hilbert-space coverage is…
First-Quantized Relativistic Quantum Simulation with Periodic and Dirichlet Boundary Conditions
Jeongho Bang, Timothy P. Spiller, Kyunghyun Baek +2
In this work, we present a methodology for first-quantized relativistic quantum simulation on one-dimensional finite domains under the two boundary conditions most commonly used in…
Quantum Occam Learning: Sample-Supported Expressibility for Circuit-Based Quantum Learning
Jeongho Bang, Kyoungho Cho, Jeongwoo Jae
A central principle in quantum machine learning is that an ansatz should be expressive enough to represent the quantum data of interest. Yet, the expressibility is statistically me…
Learning with Active Quantum Subspaces: Scalable Hybrid Advantage without Full Quantum Data-Encoding
Jeongho Bang, Wooyeong Song, Kyoungho Cho +2
We study whether quantum learning advantage can persist without fully embedding a large classical input into a highly superposed quantum state. To address this question, we introdu…