arXiv subjects

Yimin Lu

Publications and source records attributed to Yimin Lu.

4 recordsLinked to original sources

arXiv2026

Intrinsic locality dimension of quantum codes

Quantum error-correcting codes are a cornerstone of quantum computing, with broad and profound connections to physics and mathematics. In this work, we introduce the notion of intrinsic locality dimension of stabilizer codes, which is independent of the underlying geometry of quantum codes and naturally extends to non-integer values. Drawing on mathematical tools from fractal geometry and geometric measure theory, the intrinsic locality dimension accommodates flexible architectures and provides a quantitative measure of code connectivity, encompassing both topological codes and algebraic constructions such as bivariate-bicycle-type codes. We show how the intrinsic dimension serves as a fundamental organizing parameter that unifies code properties. In particular, we prove general limitations on code parameters and compatible fault-tolerant logical gates induced by the intrinsic dimension, generalizing the Bravyi--Poulin--Terhal and Bravyi--K\"{o}nig bounds for regular topological codes, respectively. Furthermore, we consider implications on thermal properties: toward fully characterizing the geometry requirement for self-correcting quantum memories (SCQMs), we present a conditional no-go result for SCQMs in dimension $3-\epsilon$ and take stock of existing results on low-dimensional SCQMs. Our theory provides a unifying mathematical framework for understanding the fundamental capabilities and geometric implementations of quantum error correction and fault tolerance.

quant-ph↗

arXiv2026

Generative modeling of granular flow on inclined planes using conditional flow matching

Granular flows govern many natural and industrial processes, yet their interior kinematics and mechanics remain largely unobservable, as experiments access only boundaries or free surfaces. Conventional numerical simulations are computationally expensive for fast inverse reconstruction, and deterministic models tend to collapse to over-smoothed mean predictions in ill-posed settings. This study, to the best of the authors' knowledge, presents the first conditional flow matching (CFM) framework for granular-flow reconstruction from sparse boundary observations. Trained on high-fidelity particle-resolved discrete element simulations, the generative model is guided at inference by a differentiable forward operator and a novel sparsity-aware gradient guidance mechanism. This mechanism avoids the gradient dilution inherent to standard mean-squared-error approaches, preserves the absolute physical scale of observation errors, enforces measurement consistency without hyperparameter tuning, and prevents unphysical velocity predictions in non-material regions. A physics decoder maps the reconstructed velocity fields to stress states and energy fluctuation quantities, including mean stress, deviatoric stress, and granular temperature. The framework accurately recovers interior flow fields from full observation to only 16\% of the informative window, and it remains effective under strongly diluted spatial resolution with only 11% of data. It also outperforms a deterministic CNN baseline in the most ill-posed reconstruction regime and provides spatially resolved uncertainty estimates through ensemble generation. These results demonstrate that conditional generative modeling offers a practical route for non-invasive inference of hidden bulk mechanics in granular media, and it suggests potential applicability for inverse problems in particulate and multiphase systems.

cs.CE↗

arXiv2025

PhantomFetch: Obfuscating Loads against Prefetcher Side-Channel Attacks

The IP-stride prefetcher has recently been exploited to leak secrets through side-channel attacks. It, however, cannot be simply disabled for security with prefetching speedup as a sacrifice. The state-of-the-art defense tries to retain the prefetching effect by hardware modification. In this paper, we present PhantomFetch as the first prefetching-retentive and hardware-agnostic defense. It avoids potential remanufacturing cost and enriches applicability to off-the-shelf devices. The key idea is to directly break the exploitable coupling between trained prefetcher entries and the victim's secret-dependent loads by obfuscating the sensitive load effects of the victim. The experiment results show that PhantomFetch can secure the IP-stride prefetcher with only negligible overhead.

cs.CR↗

arXiv2025

Grain volume distribution alters the critical phenomena in complex granular systems

The grain size distribution (GSD) plays an important role in the mechanical properties of amorphous disordered systems and complex granular materials. Varying GSD causes segregation issues and alters critical behaviors. This work used the discrete element method (DEM) to investigate the rheological and critical behaviors of sheared granular flows with various GSDs. The results show that, while a unified rheological relation can be obtained, a characteristic length scale, which is associated with the contact probability and can be obtained from any GSD, is embedded within such a polydisperse disordered system. We further acquire a correlation function between critical solid fractions and dimensionless grain volume distributions. This work elucidates the effect of particle volumes on the rheology and micromechanics of dry granular systems and provides further insights in better incorporating the influence of other particle properties into a unified framework, which is helpful and critical for the corresponding engineering and geophysical problems.

cond-mat.soft↗