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Chae-Hyun Yoon

Publications and source records attributed to Chae-Hyun Yoon.

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Ground-State Energy Estimation of HeH$^{+}$, ArH$^{+}$, and H$_2$O via Sample-Based Quantum Diagonalization

Accurate ground-state energies are essential for understanding molecular structure, chemical bonding, and reaction energetics in quantum chemistry. In this work, we investigate the ground-state properties of the molecular systems HeH$^+$, ArH$^+$, and H$_2$O using Sample-Based Quantum Diagonalization (SQD), a hybrid quantum-classical framework designed for near-term quantum devices. Unlike variational approaches such as VQE, which require deep parameterized circuits and repeated expectation-value measurements, SQD reconstructs a low-energy determinant subspace directly from measured bitstrings. For the present calculations, bitstrings were generated on IBM quantum hardware using shallow local unitary cluster Jastrow (LUCJ) circuits whose parameters were constructed from the $t_1$ and $t_2$ amplitudes of coupled-cluster singles and doubles (CCSD) calculations based on restricted Hartree--Fock (RHF) references. From these samples, we compute ground-state potential-energy curves of HeH$^+$, ArH$^+$, and H$_2$O with the 6-31G and cc-pVDZ basis sets. For all three systems, the SQD results obtained with the cc-pVDZ basis closely follow the corresponding same-basis CCSD energies and reproduce the equilibrium-region trends of the potential-energy curves. HeH$^+$ and ArH$^+$ were chosen as simple yet astrophysically important molecular-ion benchmarks, while H$_2$O was included as a representative polyatomic molecule to assess the applicability of SQD beyond diatomic ionic systems. At the adopted equilibrium geometries, the deviations from the same-active-space CASCI references are 0.00, 2.51, and 6.34 mHa for HeH$^+$, ArH$^+$, and H$_2$O, respectively. These results demonstrate the feasibility of hardware-assisted SQD for the present benchmark systems and motivate further studies of its accuracy and computational scaling for larger molecular active spaces.

physics.chem-ph

Deciphering Super El Niño: Development of a Novel Predictive Model Integrating Local and Global Climatic Signals

In recent years, extreme weather events have surged, highlighting the urgent need for action on the climate emergency. The year 2023 saw record-breaking global temperatures, unprecedented heatwaves in Europe, devastating floods in Asia, and severe wildfires in North America and Australia. Super El Niño events, known for their profound impact on global weather, play a critical role in these changes, causing severe economic and environmental damage. This study presents a novel predictive model that integrates systematically local and global climatic signals to forecast Super El Niño events, introducing the Super El Niño Index (SEI), which value of 80 or higher defines a Super El Niño event. Our analysis shows that the SEI accurately reflects past Super El Niño events, including those from 1982-83, 1997-98, and 2015-16, with SEI values for these periods containing 80 within the 2-sigma standard deviation. Using data up to 2022, our model predicted an SEI of around 80 for 2023, indicating a Super El Niño for the 2023-24 period. Recent observations confirm that the 2023-24 El Niño is among the five strongest recorded Super El Niño events in history. An analysis of SEI trends from 1982 to 2023 reveals a gradual increase, with recent El Niño events consistently exceeding SEI values of 70. This trend suggests that El Niño events are increasingly approaching Super El Niño intensity, potentially due to more favorable conditions in the equatorial Pacific. This increase in SEI values and the frequency of stronger El Niño events may be attributed to the ongoing effects of global warming. These findings emphasize the need for heightened preparedness and strategic planning to mitigate the impacts of future Super El Niño events, which are likely to become more frequent in the coming decades.

physics.ao-ph