arXiv subjects

Jianyi Lin

Publications and source records attributed to Jianyi Lin.

8 recordsLinked to original sources

arXiv2026

After Theft: From Revocation to Neutralization in the Custody of Quantum Clones

Encrypted quantum cloning enables the creation of multiple encrypted clones of an unknown quantum state while allowing only one effective decryption, with the decryption resource being intrinsically consumed in the process. In this paper, we argue that this property supports a distinctive custodial security primitive for post-compromise response. We consider a threat model in which an adversary steals an encrypted quantum clone but does not yet possess the corresponding decryption key. In such a scenario, a legitimate custodian may unseal a different trusted clone, thereby exhausting the sole available unsealing opportunity and rendering the stolen clone permanently useless. We argue that this mechanism is not adequately described as mere revocation. Rather, it realizes a stronger form of post-theft response, which we call neutralization. We formalize this distinction, locate it within a broader post-theft response space, and introduce a temporal threat model. We compare the mechanism with its closest classical analogue, showing that the classical case can reproduce the policy outcome only through external procedural composition. We finally interpret encrypted quantum cloning as a primitive for post-compromise quantum custody in distributed preservation settings, with prospective relevance for Cyber-Humanities-oriented preservation architectures.

quant-ph↗

arXiv2026

Beyond the Canonical Protocol: Quantum Encrypted Cloning from Secret-Sharing Access Structures

Quantum encrypted cloning shows that an unknown quantum state can be distributed into multiple encrypted copies without contradicting the no-cloning theorem: each copy is unusable on its own, but can be redeemed together with a suitable quantum key. Recent work has related canonical encrypted-cloning protocols to particular forms of quantum secret sharing. Here we take the converse perspective: instead of mapping a given encrypted-cloning protocol into QSS, we use QSS access structures as a design library from which encrypted-cloning schemes can be extracted. The criterion is access-structural. A QSS scheme supports a quantum encrypted-cloning structure whenever it contains a family of qualified sets with a non-qualified common intersection. The common subsystem is interpreted as the key, while the non-common parts are interpreted as encrypted clones relative to that key. Thus quantum encrypted cloning does not require a new notion of recoverability beyond QSS; what changes is the operational reading of QSS constituents as a mechanism for delayed and alternative redemption opportunities. This viewpoint separates redemption from perfect secrecy. Perfect QSS yields encrypted-cloning schemes with forbidden non-qualified subsystems, whereas ramp QSS naturally allows intermediate, partially informative non-redeeming subsystems. The resulting framework broadens quantum encrypted cloning from a specific protocol to a general access-structure primitive. We illustrate the extraction principle with threshold-like, ramp, hierarchical, and compartmented architectures, showing how encrypted clones may be symmetric or asymmetric, individual or composite, perfectly hidden or leaky. Equivalently, these constructions can be viewed as overlapping erasure-recovery regions of an isometric quantum code. This establishes secret sharing as a systematic design language for encrypted quantum redundancy.

quant-ph↗

arXiv2026

Full characterization of informative subsets in Quantum Encrypted Cloning

Quantum encrypted cloning, introduced by Yamaguchi and Kempf, is a Pauli-based protocol that distributes an unknown input qubit into multiple encrypted signal-noise pairs in such a way that redundancy is created without violating the no-cloning theorem, since at most one clone can later be perfectly recovered through an appropriate decoding procedure. In previous work we showed that unauthorized subsets of the storage register are not, in general, completely uninformative, and we identified a parity-dependent leakage pattern. In the present work we extend the analysis to subsets that also include the transformed source qubit A. Exploiting the purity of the global encoded state and the complementarity between storage-only subsets and subsets containing A, we derive a full classification of the informativeness of all sets of the form $H=\{A\}\cup C$. We show that these subsets are fully informative in the generic case. Two exceptions arise. First, if all pairs are incomplete and |C|<n, then the reduced state is completely uninformative. Second, if |C|=n, n is odd, and the number q of signal qubits in C is even, then the reduced state is partially informative. In this latter case, the residual dependence on the input state is confined to the y-component of the Bloch vector. These results provide a complete parity-based characterization of leakage for subsets containing the transformed input qubit.

quant-ph↗

arXiv2026

Geometry-Aware Physics-Informed PointNets for Modeling Flows Across Porous Structures

Predicting flows that occur both through and around porous bodies is challenging due to coupled physics across fluid and porous regions and the need to generalize across diverse geometries and boundary conditions. We address this problem using two Physics Informed learning approaches: Physics Informed PointNets (PIPN) and Physics Informed Geometry Aware Neural Operator (P-IGANO). We enforce the incompressible Navier Stokes equations in the free-flow region and a Darcy Forchheimer extension in the porous region within a unified loss and condition the networks on geometry and material parameters. Datasets are generated with OpenFOAM on 2D ducts containing porous obstacles and on 3D windbreak scenarios with tree canopies and buildings. We first verify the pipeline via the method of manufactured solutions, then assess generalization to unseen shapes, and for PI-GANO, to variable boundary conditions and parameter settings. The results show consistently low velocity and pressure errors in both seen and unseen cases, with accurate reproduction of the wake structures. Performance degrades primarily near sharp interfaces and in regions with large gradients. Overall, the study provides a first systematic evaluation of PIPN/PI-GANO for simultaneous through-and-around porous flows and shows their potential to accelerate design studies without retraining per geometry.

cs.LG↗

arXiv2023

Sparse Models for Machine Learning

The sparse modeling is an evident manifestation capturing the parsimony principle just described, and sparse models are widespread in statistics, physics, information sciences, neuroscience, computational mathematics, and so on. In statistics the many applications of sparse modeling span regression, classification tasks, graphical model selection, sparse M-estimators and sparse dimensionality reduction. It is also particularly effective in many statistical and machine learning areas where the primary goal is to discover predictive patterns from data which would enhance our understanding and control of underlying physical, biological, and other natural processes, beyond just building accurate outcome black-box predictors. Common examples include selecting biomarkers in biological procedures, finding relevant brain activity locations which are predictive about brain states and processes based on fMRI data, and identifying network bottlenecks best explaining end-to-end performance. Moreover, the research and applications of efficient recovery of high-dimensional sparse signals from a relatively small number of observations, which is the main focus of compressed sensing or compressive sensing, have rapidly grown and became an extremely intense area of study beyond classical signal processing. Likewise interestingly, sparse modeling is directly related to various artificial vision tasks, such as image denoising, segmentation, restoration and superresolution, object or face detection and recognition in visual scenes, and action recognition. In this manuscript, we provide a brief introduction of the basic theory underlying sparse representation and compressive sensing, and then discuss some methods for recovering sparse solutions to optimization problems in effective way, together with some applications of sparse recovery in a machine learning problem known as sparse dictionary learning.

cs.LG↗

arXiv2021

Local limit laws for symbol statistics in bicomponent rational models

We study the local limit distribution of the number of occurrences of a symbol in words of length $n$ generated at random in a regular language according to a rational stochastic model. We present an analysis of the main local limits when the finite state automaton defining the stochastic model consists of two primitive components. The limit distributions depend on several parameters and conditions, such as the main constants of mean value and variance of our statistics associated with the two components, and the existence of communications from the first to the second component. The convergence rate of these results is always of order $O(n^{-1/2})$. We also prove an analogous $O(n^{-1/2})$ convergence rate to a Gaussian density of the same statistic whenever the stochastic models only consists of one (primitive) component.

math.PR↗

arXiv2010

Electronic Structure and Structural Evolutions of Hydrogenated Graphene Probed by Raman Spectroscopy

The electronic structure and structural evolution of hydrogenated graphene are investigated by Raman spectroscopy with multiple excitations. The excitation energy dependent saturation effect on the ratio of integrated intensities of D and G modes (ID/IG) is revealed and further developed as a quick method for estimation of inter-defect distance and defect density in hydrogenated graphene. At low hydrogen coverage, the chemisorbed H atoms behave like defects in sp2 C=C matrix; while for a high hydrogen coverage, the sp3 C-H bonds become coalescent clusters, resulting in confinement effect on the sp2 C domains. Electronic structure changes caused by varying hydrogen coverage are evidenced by excitation energy dependent red shift of D and 2D bands. Our results provide a useful guide for developing applications of hydrogenated graphene, as well as using Raman spectroscopy as quick metrology of the defect density in further exploring other kinds of graphene derivatives.

cond-mat.mtrl-sci↗

arXiv2010

Large Scale Synthesis of Bi-layer Graphene in Strongly Coupled Stacking Order

Large scale synthesis of single layer graphene (SLG) by chemical vapor deposition (CVD) has received a lot of attention recently. However, CVD synthesis of AB stacked bi-layer graphene (BLG) is still a challenging work. Here we report synthesis of BLG homogeneously in large area by thermal CVD. The 2D Raman band of CVD BLG splits into four components, suggesting splitting of electronic bands due to strong interlayer coupling. The splitting of electronic bands in CVD BLG is further evidenced by the study of near infrared (NIR) absorption and carrier dynamics probed by transient absorption spectroscopy. Ultraviolet photoelectron spectroscopy invesigation also indiates CVD BLG possesses different electronic structures from those of CVD SLG. The growth mechanism of BLG is found to be related to catalystic activity of copper (Cu)surface, which is determined by purity of Cu foils employed in CVD process. Our work showsthat strongly coupled or even AB stacked BLG can be grown on Cu foils in large scale, which isof particular importance for device applications based on their split electronic bands

cond-mat.mtrl-sci↗