arXiv subjects

Yuting Lei

Publications and source records attributed to Yuting Lei.

3 recordsLinked to original sources

arXiv2026

Geometry-Resolved Projection of RF Imbalance to Ion Micromotion in a Same-Phase Dual-RF Blade Trap

Common-mode metrics of a high-$Q$ helical resonator do not determine the residual ion-side field in a dual-electrode drive. We combine a two-node differential RF model with single-electrode finite-element bases to obtain a computation-only, geometry-resolved projection for a same-phase blade trap. For a 3.5 pF external load per branch, the model gives a total effective branch capacitance of 7.640 pF and an HWHM-equivalent full branch-difference scale of 12.7 fF at $Q_{\mathrm{loaded}}=600$. The seven-segment geometry gives center and axial-RMS differential field coefficients of 640 V m$^{-1}$ and 635 V m$^{-1}$ per differential peak volt. A representative 10 fF mismatch with an effective 0.1 pF balance scale projects to 44.5/44.1 nm center/RMS $^{171}\mathrm{Yb}^{+}$ micromotion at 100 V common peak voltage. Supplementary thermal, bypass-admittance, and tested numerical cases characterize model sensitivity. All reported displacements are projections; no RF-bench or ion-side validation is claimed.

quant-ph↗

arXiv2026

Boundary-Phase Control of Sequentially Addressed Trapped-Ion ZZ Interactions

Motion-mediated trapped-ion interactions commonly coordinate state-dependent forces on both target ions. Sequential optical access reduces the number of concurrent target channels but makes the relative phase between disjoint force windows a control variable. We derive a complex near-resonant description in which each window generates a displacement vector and ordered symplectic products between vectors on different ions produce the ZZ phase. Only relative boundary phases affect this area; a common phase shift is a gauge transformation. Building on the experimental precedent for alternating single-ion addressing, we develop matched-envelope phase and contrast controls that isolate this boundary-phase dependence without target-window overlap or hidden force in the dark gaps. The analysis separates phase generation from differential closure, projector-common motion, deterministic local-Z phases, spectator coupling, and control-parameter transfer. A conditional-Ramsey sequence gives continuous and reset contrasts of 0.998 and 0.996, with a reset-induced phase separation of 0.581 rad modulo $\pi/2$. In the representative comparison, sequential control uses fewer concurrent target channels but greater normalized force action than independently calibrated simultaneous control. All results are model-level estimates within the stated Lamb-Dicke, rotating-wave, and apparatus-input limits.

quant-ph↗

arXiv2026

ERNIE-Image Technical Report

We introduce ERNIE-Image, an open-source text-to-image generation model built upon an 8B single-stream DiT architecture. ERNIE-Image aims to bridge the gap between current open-source models and leading closed-source systems through more effective mining of large-scale pre-training data and improved supervision quality throughout training. During pre-training, we adopt a bottom-up data construction pipeline that combines fine-grained image categorization, rich caption annotation, aesthetic assessment, and hierarchical sampling. This strategy reduces data noise while preserving long-tail concepts and detailed real-world knowledge, providing a stronger foundation for complex generation tasks. In the post-training stage, we use a top-down data construction pipeline for high-demand scenarios, diversify prompt annotations to better match real user inputs, and apply a stabilized DPO strategy to align the model with human aesthetic preferences. We further train ERNIE-Image-Turbo for efficient 8-NFE generation and propose MT-DMD to mitigate capability drift during distillation. To make the model easier to use in practical scenarios, we equip it with a lightweight Prompt Enhancer that expands concise user intents into structured visual descriptions. In addition, we develop ERNIE-Image-Aes, an industrial-grade aesthetic model, together with ERNIE-Image-Aes-1K, a human-annotated benchmark for realistic aesthetic evaluation. Extensive qualitative and quantitative experiments show that ERNIE-Image achieves leading performance among open-source models and approaches top-tier commercial models in instruction following, text rendering, and aesthetic quality. We release the trained models and aesthetic resources to facilitate further academic research and technical progress in the AIGC community.

cs.CV↗