arXiv subjects

Ziheng Ma

Publications and source records attributed to Ziheng Ma.

3 recordsLinked to original sources

arXiv2026

Emulation of the dynamics of bound electron exposed to strong oscillatory laser field with Bose-Einstein Condensates

This paper employs a Bose-Einstein condensates to simulate the dynamical response of bound electrons in a strongly oscillating pulsed laser field. We investigate the excitation dynamics of Bose-Einstein condensates with repulsive interaction confined in a potential well with finite depth and width driven by a strong oscillatory pulse field. By numerically solving the Gross-Pitaevskii equation with Crank-Nicolson method and split operator method, we obtain the time-dependent wavefunction and therefore the evolution of density distribution in real space and that in momentum space, and the occupation distribution in energy space. It is shown that cold atoms with weak interaction oscillate as a whole body in a finite space when the amplitude of pulse drive is not strong enough. During the evolution atoms occupy the bound states with larger probability. Increasing the driving strength or atomic interactions promotes the excitation of atoms into continuum states and their diffusion out of the potential well, leading to complex structures or even interference-like patterns in the momentum distribution. The number of cycles in the pulse envelope plays a crucial role in the dynamical behavior: High-frequency driving can suppress diffusion and maintain localization. Furthermore, repulsive atomic interactions can enhance high-harmonic generation yields by several orders of magnitude. This study offers a new perspective for quantum simulations of ultrafast dynamics in strong fields and reveals the regulatory role of interactions in condensates on non-equilibrium dynamical processes.

cond-mat.quant-gas↗

arXiv2025

Improving Aufbau Suppressed Coupled Cluster Through Perturbative Analysis

Guided by perturbative analysis, we improve the accuracy of Aufbau suppressed coupled cluster theory in simple single excitations, multi-configurational single excitations, and charge transfer excitations while keeping the cost of its leading-order terms precisely in line with ground state coupled cluster. Combining these accuracy improvements with a more efficient implementation based on spin-adaptation, we observe high accuracy in a large test set of single excitations, and, in particular, a mean unsigned error for charge transfer states that outperforms equation-of-motion coupled cluster theory by 0.25 eV. We discuss how these results are achieved via a systematic identification of which amplitudes to prioritize for single- and multi-configurational excited states, and how this prioritization differs in important ways from the ground state theory. In particular, our data show that a partial linearization of the theory increases accuracy by mitigating unwanted side effects of Aufbau suppression.

physics.chem-ph↗

arXiv2023

Label Information Enhanced Fraud Detection against Low Homophily in Graphs

Node classification is a substantial problem in graph-based fraud detection. Many existing works adopt Graph Neural Networks (GNNs) to enhance fraud detectors. While promising, currently most GNN-based fraud detectors fail to generalize to the low homophily setting. Besides, label utilization has been proved to be significant factor for node classification problem. But we find they are less effective in fraud detection tasks due to the low homophily in graphs. In this work, we propose GAGA, a novel Group AGgregation enhanced TrAnsformer, to tackle the above challenges. Specifically, the group aggregation provides a portable method to cope with the low homophily issue. Such an aggregation explicitly integrates the label information to generate distinguishable neighborhood information. Along with group aggregation, an attempt towards end-to-end trainable group encoding is proposed which augments the original feature space with the class labels. Meanwhile, we devise two additional learnable encodings to recognize the structural and relational context. Then, we combine the group aggregation and the learnable encodings into a Transformer encoder to capture the semantic information. Experimental results clearly show that GAGA outperforms other competitive graph-based fraud detectors by up to 24.39% on two trending public datasets and a real-world industrial dataset from Anonymous. Even more, the group aggregation is demonstrated to outperform other label utilization methods (e.g., C&S, BoT/UniMP) in the low homophily setting.

cs.AI↗