arXiv subjects

Ruibin Xu

Publications and source records attributed to Ruibin Xu.

6 recordsLinked to original sources

arXiv2026

Sliding contact creates universal self-affine fractal surfaces

Surface roughness evolves during sliding, a process known as run-in, and the resulting topography controls friction, leakage, and failure from machines to geological faults. Yet the physical rule selecting this state remains unclear. We show that metals, rocks, and glasses develop universal self-similar roughness at short wavelengths, while retaining a material-dependent roll-off. A two-process model explains this behavior: junction formation and rupture drive universal roughening, whereas larger-scale deformation and/or fracture limit its growth.

cond-mat.soft↗

arXiv2026

Adhesion-controlled sliding and the Stribeck curve in hydrophobic soft contacts

We present an experimental and theoretical study of dry and glycerol-lubricated sliding for polymethyl methacrylate (PMMA) cylinders with different surface roughness sliding on polydimethylsiloxane (PDMS) rubber. This system represents a hydrophobic soft contact, where adhesion may persist even in the presence of the lubricant and thereby modify both the real contact area and the sliding response. Dry-friction measurements, combined with contact-area calculations that include adhesion, provide a baseline for the lubricated study. For the two sandblasted surfaces, the measured Stribeck curves are described reasonably well by a mean-field mixed-lubrication theory with a fitted velocity-independent effective interfacial shear stress. In contrast, the smooth surface exhibits qualitatively different behavior. We attribute this to an adhesion-controlled sliding mode involving macroscopic Schallamach-wave-like instabilities at low sliding speeds, which are progressively suppressed as the sliding speed increases and forced wetting reduces direct solid-solid contact. The results show that, for soft hydrophobic contacts, the Stribeck curve cannot always be understood from classical fluid flow and load sharing alone. For sufficiently smooth and adhesive surfaces, adhesion changes not only the real contact area but also the sliding mode itself.

cond-mat.soft↗

arXiv2026

A Dynamical Lie-Algebraic Framework for Hamiltonian Engineering and Quantum Control

Determining the unitary dynamics accessible from finite Hamiltonian resources is a central problem in Hamiltonian engineering and quantum control. Dynamical Lie algebras (DLAs) connect available control Hamiltonians with the reachable dynamics, but their use as a design tool for modifying Hamiltonian generator sets remains less developed. In this work, we develop a finite-dimensional DLA framework for three generator-set operations: composition, invariance, and reduction. For composition, we construct direct sums of component DLAs using spectral projectors on an auxiliary register. For invariance, we analyze when modifications of Pauli-string generating sets preserve the generated Lie algebra, and introduce algebraic diagnostics for added generators. For reduction, we consider compact reductive DLAs and show how projection onto selected simple ideals gives reduced generating sets whose Lie closures are the corresponding ideal sums. We illustrate these results with finite-dimensional examples and numerical checks, including direct-sum dimension addition, central-spin invariance diagnostics, and DLA-based ansatz reduction for block-local Hamiltonians. The results show how DLA structure can be used to diagnose controllability and guide Hamiltonian generator design under explicit algebraic assumptions.

quant-ph↗

arXiv2025

Entangled mixed-state datasets generation by quantum machine learning

The advancement of classical machine learning is inherently linked to the establishment and progression of classical dataset. In quantum machine learning (QML), there is an analogous imperative for the development of quantum entangled datasets comprised with huge quantity and high quality. Especially for multipartite mixed-state datasets, due to the lack of suitable entanglement criteria, previous researchers often could only perform classification tasks on datasets extended based on Werner states or other well-structured states. This paper is dedicated to provide a method for generating mixed-state datasets for entangled-separable classification tasks. This method is based on supervised quantum machine learning and the concentratable entanglement measures. It furthers the assembly of quantum entangled datasets, inspires the discovery of new entanglement criteria with both classical and quantum machine learning, and provides a valuable resource for benchmarking QML models, thereby opening new avenues for exploring the rich structure of quantum entanglement in mixed states. Additionally, we benchmark several machine learning models using this dataset, offering guidance and suggestions for the selection of QML models.

quant-ph↗

arXiv2024

Sliding wear: role of plasticity

We present experimental wear data for polymethyl methacrylate (PMMA) sliding on tile, sandpaper, and polished steel surfaces, as well as for soda-lime, borosilicate, and quartz glass sliding on sandpaper. The results are compared with a recently developed theory \cite{ToBe} of sliding wear based on crack propagation (fatigue), originally formulated for elastic contact and here extended to include plasticity. The elastoplastic wear model predicts wear rates that agree reasonably well with the experimental results for PMMA and soda-lime glass. However, deviations observed for quartz suggest that material-specific deformation mechanisms, particularly the differences between crystalline and amorphous structures, may need to be considered for accurate wear predictions across different materials. In addition, the model reveals a non-monotonic dependence of the wear rate on the penetration hardness $\sigma_{\rm P}$. Thus, for plastically soft material, the wear rate increases with increasing $\sigma_{\rm P}$, while for hard materials, it decreases. This contrasts with Archard's wear law, where the wear rate decreases monotonically with increasing $\sigma_{\rm P}$.

cond-mat.soft↗

arXiv2024

Brownian friction dynamics: fluctuations in sliding distance

We have studied the fluctuation (noise) in the position of sliding blocks under constant driving forces on different substrate surfaces. The experimental data are complemented by simulations using a simple spring-block model where the asperity contact regions are modeled by miniblocks connected to the big block by viscoelastic springs. The miniblocks experience forces that fluctuate randomly with the lateral position, simulating the interaction between asperities on the block and the substrate. The theoretical model provides displacement power spectra that agree well with the experimental results.

cond-mat.soft↗