10 papers
Optimal and Deterministic Quantum Search on the Simplex of Complete Graphs
Kiyoji Huang Fujiwara, Yujia Shi, Thomas G. Wong
The simplex of complete graphs, also known as the first-order truncated simplex lattice, is a network of identical complete graphs, each with vertices, such that each cli…
FreeAnimate: Training-Free Human Image Animation with Preview-Guided Denoising
Yuan Zeng, Yujia Shi, Zongqing Lu +1
Human Image Animation has seen significant advancements, primarily driven by diffusion models. However, existing methods typically demand substantial training data and resources to…
EgoPressDiff: Multimodal Video Diffusion for Egocentric UV-Domain Hand-Pressure Estimation
Yuan Zeng, Zilue Gao, Yujia Shi +3
Estimating hand-surface contact pressure from an egocentric view is crucial for AR/VR devices, robotic imitation, and ergonomic analysis. Existing methods often discretize pressure…
Self-Trapping Bounds for Continuous-Time Nonlinear Quantum Walks on Path and Cycle Graphs
Yujia Shi, Thomas G. Wong
We explore a continuous-time quantum walk starting at a single vertex on the discrete path and cycle with a cubic nonlinearity. Such nonlinearities arise in Bose-Einstein condensat…
Fast state transfer via loop weights
Gabor Lippner, Yujia Shi
We prove that almost-linear-time high-fidelity state transfer is achievable in a quantum spin chain using loop weights at the second and second-to-last nodes. We provide specific p…
Resilience by Design: A KPI for Heavy-Duty Megawatt Charging
Sonia Yeh, Rishabh Ghotge, Yujia Shi +1
We introduce a stressor-agnostic Resilience Key Performance Indicator (Resilience KPI) for megawatt charging stations (MSC) serving heavy-duty vehicles. Beyond routine performance…
Continuous-Time Quantum State Transfer with a Generalized Laplacian
Yujia Shi
Quantum walks generated by the adjacency matrix or the Laplacian are known to exhibit low transfer fidelity on general graphs. In this paper, we study continuous-time quantum walks…
Data Aware Differentiable Neural Architecture Search for Tiny Keyword Spotting Applications
Yujia Shi, Emil Njor, Pablo Martínez-Nuevo +2
The success of Machine Learning is increasingly tempered by its significant resource footprint, driving interest in efficient paradigms like TinyML. However, the inherent complexit…
Strong quantum state transfer on graphs via loop edges
Gabor Lippner, Yujia Shi
We quantify the effect of weighted loops at the source and target nodes of a graph on the strength of quantum state transfer between these vertices. We give lower bounds on loop we…
Quantifying State Transfer Strength on Graphs with Involution
Gabor Lippner, Yujia Shi
This paper discusses continuous-time quantum walks and asymptotic state transfer in graphs with an involution. By providing quantitative bounds on the eigenvectors of the Hamiltoni…