arXiv subjects

Long Huang

Publications and source records attributed to Long Huang.

At least 19 recordsLinked to original sources

arXiv2026

Probing Coherent Noise with Stabilizer Codes: Identifiability, Ambiguity, and Decoder Priors

Coherent noise calls for a reusable physical model, but Pauli twirling erases it and full process tomography scales exponentially. We turn measurement-free quantum error correction into a probe: bit-flip and phase-flip codes polarize few-qubit coherent noise into single-logical-qubit channels, and the resulting fingerprint identifies an 11-parameter three-qubit family. An exact rational-arithmetic rank certificate, evaluated at a rational point in the interior of the parameter domain, proves local injectivity on a dense open subset of that domain. The map is nevertheless not globally injective: a compensating rotation combined with a site exchange yields exact collisions, which we classify and then remove by adding one two-qubit marginal channel. The site exchange is the only nontrivial permutation symmetry of the $n$-qubit chain generator at any $n$, so the classification is structural rather than a three-qubit accident. A Cramér--Rao analysis, checked at the level of individual measurement settings, reaches $0.01$~rad per parameter at a representative point, and decoder priors rebuilt from such an estimate retain most of their correlation gain. Propagating the generator into a distance-three surface code gives a $14.8$-fold encoding gain when the generator is known exactly, and $1.21$-fold of the correlation gain survives when the prior is rebuilt from an estimate at the stated precision, closing the loop from characterization to decoding.

quant-ph↗

arXiv2026

Noise redistribution by stabilizer codes: an organizing view of concatenated three-qubit codes

We formulate stabilizer-code error correction as a code- and recovery-dependent transformation of the noise channel. For a fixed recovery map, syndrome projection produces a block-diagonal logical channel whose diagonal Pauli weights follow from code-induced error classes, with no physical Pauli twirl; concatenation is then iterated redistribution, with the dominant logical error guiding the next code. The bit-flip and phase-flip three-qubit codes with standard recoveries supply a closed-form benchmark, and logically inequivalent recoveries appear as a design degree of freedom for shaping the channel presented to the next decoder. For amplitude damping, the three-qubit rules select $C_2(\hatσ_y)C_1(\hatσ_x)$ and give $f_l=1-\mathcal O(γ^2)$, with an ideal-gate crossover at $γ_c\approx0.30$ ($f=1-1.72755γ^2$) and a favorable gate-count comparison with the five-qubit code under the same assumptions. Correlated noise requires joint class statistics, whereas coherent noise retains intra-class coherences that make the same transformation usable in reverse as a tomographic fingerprint; there the companion work proves local identifiability from a rank certificate evaluated inside the parameter domain, classifies the sole permutation-symmetry ambiguity as structural at any chain length, and closes it with one two-qubit marginal channel. The construction remains an analytic benchmark, not a universal prescription for an inner code beneath a surface code.

quant-ph↗

arXiv2026

Hausdorff content $A_p$ weights: weighted maximal inequalities and fundamental structural properties

Let $\mathcal H_{\infty}^δ$ denote the Hausdorff content of dimension $δ\in(0,n]$ defined on subsets of $\mathbb R^n$. The principal problem, considered in this paper, is to characterize the non-negative function $w$ for which the weighted $L^p$ inequality with $p\in(1,\infty)$ and the weighted weak $L^1$ inequality on Hardy-Littlewood maximal operators associated with Hausdorff content hold true. To achieve this, we introduce a class of Hausdorff content $A_p$ weights, denoted as $\mathcal A_{p,δ}$, and then show that, for any $p\in(1,\infty)$ and $δ\in(0,n]$, the weighted $L^p$ maximal inequality holds true if and only if $w\in\mathcal A_{p,δ}$, and the weighted weak $L^1$ maximal inequality holds true if and only if $w\in\mathcal A_{1,δ}$ by proposing a novel approach. As the second objective, applying this approach, the seminal structural properties of classical Muckenhoupt $A_p$ weights, such as the reverse Hölder inequality [R. R. Coifman and C. Fefferman, Studia Math. 51 (1974), 241-250], the self-improving property [B. Muckenhoupt, Trans. Amer. Math. Soc. 165 (1972), 207-226], and Jones' factorization theorem [P. W. Jones, Ann. of Math. (2) 111 (1980), 511-530], are all established for $\mathcal A_{p,δ}$. The main novelties of this paper lies in: (i) we discover $\mathcal A_{p,δ}$ weights with rich structural properties, that increase strictly on dimension exponent and hence reduce to the classical $A_p$ class at the endpoint dimension, forming proper subsets of it for smaller dimension exponent; (ii) by proposing a novel approach, this work naturally extends the classical theory beyond measure-theoretic frameworks, while also provides novel insights even when reduced to the classical $A_p$ weight case.

math.CA↗

arXiv2026

Weak-$L^\infty$ and BMO with Hausdorff Content

The space of weak-$L^{\infty}$ on measure space was introduced by Bennett et al. (Ann. of Math., 113(1981), 601-611) and proved to contain the space of $L^\infty$ and possess the appropriate interpolation property. In this paper, we are aimed to continue this line of research on the Euclidean space $\mathbb R^n$ endowed with Hausdorff content, which is a low-dimensional outer measure on $\mathbb{R}^n$, and fails to be additive or absolutely continuous with respect to Lebesgue measures. But low-dimensional subsets, such as fractals, of $\mathbb{R}^n$ are Hausdorff capacitable. Precisely, in the setting of Hausdorff content, an analogue of weak-$L^\infty$ is defined and proved to possess an improved Marcinkiewicz interpolation theorem. Moreover, we show that the space of functions with bounded mean oscillation associated to the Hausdorff content is embedded into this new weak-$L^\infty$ space by establishing a key estimate for the distribution via a packing condition. As a by-product, the boundedness of the Hardy-Littlewood operator on the space of weak-$L^{\infty}$ is established.

math.CA↗

arXiv2026

Reverse Hölder Class and Two-Parameter Good-$λ$ Inequality over Hausdorff Content

Classical harmonic analysis is built upon Lebesgue (or more general) measure. Such a framework is remarkably successful for sets of full ambient dimension, but becomes intrinsically insensitive to lower-dimensional geometry: curves, surfaces, and more generally fractal subsets of $\mathbb R^n$ with zero $n$-dimensional Lebesgue measure. In contrast, Hausdorff content provides a dimension-sensitive notion of size for lower-dimensional sets or fractal sets, but harmonic analysis in this setting is still largely limited to maximal operators. In this paper, we establish a weighted two-parameter good-$λ$ inequality under Hausdorff content setting, providing a route toward other general operators from maximal operators over Hausdorff content. To do this, we firstly identify an appropriate reverse Hölder class $\mathcal R\mathcal H_{q,δ}$ associated with Hausdorff content, and then prove the self-improving property of $\mathcal R\mathcal H_{q,δ}$ by establishing the fundamental equivalence between reverse Hölder weights and capacitary Muckenhoupt weights. As direct applications, we obtain weighted Fefferman--Stein inequality and some boundedness on general operators over Hausdorff content. The methodology developed in this paper, even when reduced to the Lebesgue measure case, also provide novel insights, which shows that the good-$λ$ principle is not intrinsically tied to an underlying measure, but can extended to a non-additive framework.

math.CA↗

arXiv2026

Ultra-low-loss Ta2O5 waveguide for integrated nonlinear photonics

Integrated nonlinear photonics demands waveguides that simultaneously offer ultra-low-loss, relatively strong nonlinearity, and tight mode confinement. Tantalum pentoxide (Ta2O5) is attractive for this purpose because it combines a broad transparency window, relatively strong third-order nonlinearity, and favorable thick-film growth characteristics. In tightly confined Ta2O5 waveguides, however, enhanced interaction between the optical mode and etched sidewalls makes scattering loss an important limitation for achieving high Q anomalous-dispersion resonators. Here, we mitigate this confinement-scattering trade-off in pure Ta2O5 waveguides using a fabrication flow based on the Damascene concept. The resulting microring resonators (MRRs) with a cross-section of 1.6 μm x 0.85 μm exhibit intrinsic quality factors exceeding 4.0 x 106, corresponding to propagation losses below 10 dB/m, while maintaining anomalous dispersion suitable for bright dissipative Kerr soliton operation. Using these resonators, we generate a coherent bright single-soliton microcomb with a repetition rate of 108.32 GHz. Under optimized dual-pump operating conditions, the platform further exhibits an experimentally accessible single-soliton window spanning 7.5 GHz, indicating a large detuning tolerance for soliton operation; this broad operating window is consistent with the relatively weak thermo-optic response of Ta2O5. Thermal-bistability analysis further indicates that the residual scattering loss is comparable to the material-absorption loss, rather than dominating the loss budget. These results demonstrate that low optical loss, tight mode confinement, and dispersion engineering suitable for soliton formation can be jointly achieved in a pure Ta2O5 platform, providing an ultra-low-loss platform foundation for integrated nonlinear photonic devices and more complex chip-scale nonlinear photonic systems.

physics.optics↗

arXiv2026

The Information Rate of Fiber-Wireless Communication Systems Based on Photonic Generation of RF Signals

High-capacity fiber-wireless communication systems operating at high frequencies increasingly rely on photonic generation of radio-frequency (RF) signals. In these systems, optical signals are transmitted over optical fibers and detected by photodetectors, where RF signals are generated at frequencies equal to the difference between the optical carrier frequencies. A major performance-limiting impairment is the phase noise of the generated RF signals, which originates from the phase noise of the optical sources. In this paper, we develop comprehensive probabilistic models for the two principal configurations of fiber-wireless communication systems employing photonic RF generation. Based on these models, we propose an efficient numerical framework for calculating the information rate (IR). Numerical simulations are performed to validate the efficiency of the proposed algorithm and the results provide design guidelines for high-performance fiber-wireless systems.

physics.optics↗

arXiv2026

The distance from functions in BMO to BLO

Let BMO and BLO denote the spaces of all locally integrable real-valued functions on $\mathbb{R}^n$ with bounded mean oscillation and bounded lower oscillation, respectively. It is well known that $$L^\infty(\mathbb{R}^n)\subsetneqq {\rm BLO}\subsetneqq {\rm BMO}.$$ In 1978, Garnett and Jones gave distance formulas of $f\in {\rm BMO}$ to $L^\infty(\mathbb{R}^n)$ and recently, Angrisani studied the distance of $f\in {\rm BLO}$ to $L^\infty(\mathbb{R}^n)$. In this paper, we characterize the distance from any given function $f \in {\rm BMO}$ to BLO via the Muckenhoupt weight class $A_p$ as follows \begin{center} dist\,($f$,\ BLO)\,$\sim\inf\left\{ξ\in(0,\infty):\ e^{-\frac fξ}\in A_p\ \mathrm{for\ some\ }p\in(1,\infty)\right\}$. \end{center} Two equivalent representations of this distance are also established in terms of exponential form and the infimum of the constant in a variant of John--Nirenberg inequality, respectively.

math.CA↗

arXiv2026

From Independent to Joint: Enhancing Quantum Phase and Correlation Factor Estimation by Squeezed Reservoir Engineering

High-precision quantum parameter estimation is fundamental to the advancement of quantum metrology. Although reservoir engineering provides a powerful approach to improve estimation by tailoring system-environment interactions, the role of the squeezing phase and correlations arising from the sequential utilization of the same squeezed reservoir remains inadequately explored. In this work, we employ a correlated squeezed-thermal reservoir to enhance the precision of estimating the phase parameter $ϕ$ and the correlation factor $μ$, both individually and simultaneously. We show that the squeezing phase $Φ$ is crucial for achieving quantum-enhanced precision, with optimal phase-matching conditions that depend strongly on $μ$. Specifically, we derive the near-optimal phase-matching relations aimed at maximizing the quantum Fisher information (QFI) for both $ϕ$ and $μ$, as well as minimizing the total variance $Δ_{\rm sim}$ in joint estimation. Furthermore, we show that the joint estimation variance is dominated by $F_ϕ$, which motivates our search for the phase-matching conditions that minimize $Δ_{\text{sim}}$. Through the ratio $R$ of variances, we demonstrate that joint estimation conserves quantum resources and maintains high precision when the squeezing phase is optimized for $F_ϕ$, despite the inherent incompatibility of the parameters. These findings provide practical insights into reservoir engineering strategies for high-precision quantum sensing and information processing.

quant-ph↗

arXiv2026

Resource-Efficient Teleportation of High-Dimensional Quantum Coherence via Initial Phase Engineering

High-dimensional quantum systems leverage an expanded Hilbert space to enhance resilience against decoherence and noise. However, standard quantum teleportation is fundamentally limited by the quadratic growth of measurement complexity and high classical communication overhead, requiring the resolution of $d^2$ Bell states and $2\log_2 d$ classical bits. In this study, we propose a resource-efficient high-dimensional coherence teleportation (REHDCT) protocol. By designing $d$ sets of specialized positive operator-valued measure (POVM) bases, our protocol achieves a 50\% reduction in classical communication by utilizing one of the $d$ designed POVM sets, which effectively scales the measurement complexity from $O(d^2)$ to $O(d)$. Furthermore, we demonstrate that by utilizing initial phase engineering to align the target qudit with the measurement basis, theoretically perfect teleportation of quantum coherence can be achieved for arbitrary qudit states. A quantitative robustness analysis reveals that the protocol remains highly resilient to operational errors, maintaining an efficiency above 99.6\% even under a 0.1 rad phase deviation for $d=16$. Our analysis under various noise models (amplitude damping, phase flip, depolarizing, and dit-flip) confirms that high-dimensional systems exhibit an expanding quantum advantage window as dimensionality increases. Notably, under dit-flip noise, perfect coherence teleportation can be restored through the optimal selection of the POVM basis. These findings establish REHDCT as a practical, hardware-friendly framework for resource-efficient quantum communication in future high-dimensional networks.

quant-ph↗

arXiv2026

Capacitary Muckenhoupt Weight, BMO and BLO Spaces with Hausdorff Content, Factorization Theorems and Applications

Let $δ\in(0,n]$, $p\in[1,\infty)$, $\mathcal H_{\infty}^δ$ denote the Hausdorff content on $\mathbb R^n$, and $\mathcal A_{p,δ}$ be the capacitary Muckenhoupt weight class. We are interested in understanding the relationship between the capacitary Muckenhoupt weight class $\mathcal A_{p,δ}$ and ${\rm{BMO}}(\mathbb R^n, \mathcal H_{\infty}^δ)$ or ${\rm{BLO}}(\mathbb R^n, \mathcal H_{\infty}^δ)$ spaces for all dimension $δ\in(0,n]$, and further to comprehend the structure of these two spaces. Our main result shows that $\mathcal A_{p,δ}$ for $p\in(1,\infty)$ is equivalent to the BMO spaces, while $\mathcal A_{1,δ}$ is equivalent to the BLO spaces, and consequently yields the factorization theorems for these BMO and BLO spaces via capacitary Hardy--Littlewood maximal operators, which essentially extend main results of Coifman and Rochberg in 1980 beyond measure theory. As applications, by establishing some capacitary weighted John--Nirenberg inequalities, we obtain the equivalence between capacitary weighted BMO or BLO spaces and ${\rm{BMO}}(\mathbb R^n, \mathcal H_{\infty}^δ)$ or ${\rm{BLO}}(\mathbb R^n, \mathcal H_{\infty}^δ)$ respectively. These results reveal deep connections between $\mathcal A_{p,δ}$ and BMO or BLO spaces with Hausdorff content, beyond the classical measure-theoretic settings. We develop some approaches in the proofs and using a new observation, that is, the additivity of measures and linearity of integrals are superfluous for the corresponding classical theory.

math.CA↗

arXiv2026

Uncertainty-Aware Gradient Signal-to-Noise Data Selection for Instruction Tuning

Instruction tuning is a standard paradigm for adapting large language models (LLMs), but modern instruction datasets are large, noisy, and redundant, making full-data fine-tuning costly and often unnecessary. Existing data selection methods either build expensive gradient datastores or assign static scores from a weak proxy, largely ignoring evolving uncertainty, and thus missing a key source of LLM interpretability. We propose GRADFILTERING, an objective-agnostic, uncertainty-aware data selection framework that utilizes a small GPT-2 proxy with a LoRA ensemble and aggregates per-example gradients into a Gradient Signal-to-Noise Ratio (G-SNR) utility. Our method matches or surpasses random subsets and strong baselines in most LLM-as-a-judge evaluations as well as in human assessment. Moreover, GRADFILTERING-selected subsets converge faster than competitive filters under the same compute budget, reflecting the benefit of uncertainty-aware scoring.

cs.CL↗

arXiv2026

Quantitative Hardy--Littlewood maximal inequalities and Wiener--Stein theorem on p.c.f. fractals

Let $K\subset \mathbb{R}^d$ be a post-critically finite (p.c.f.) self-similar set with Hausdorff dimension $s$, and $μ$ be a self-similar probability measure supported on $K$. Let $H^α_μ$, $0<α\le s$, be the Hausdorff content on $K$, and $M_{\mathcal{D}}^μ$ be the Hardy--Littlewood maximal operator defined on $K$ associated with its basic cubes $\mathcal{D}$. In this paper, we establish quantitative strong type and weak type Hardy--Littlewood maximal inequalities on fractal set $K$ with respect to $H^α_μ$ for all range $0<α\le s$. As applications, the Lebesgue differentiation theorem on $K$ is proved. Moreover, via the Hardy--Littlewood maximal operator $M_{\mathcal{D}}^μ$, we characterize the Lebesgue--Choquet space $L^p(K,H^α_μ)$ and the Zygmund space $L\log L(K,μ)$. To be exact, given $α/s< p\le \infty$, we discover that \[ \text{$f\in L^p(K,H^α_μ)$ if and only if $M_{\mathcal{D}}^μf\in L^p(K,H^α_μ)$}\] and, for $f\in L^1(K,μ)$ with $K$ satisfying the strong separation condition, \[\text{$M_{\mathcal{D}}^μf\in L^1(K,μ)$ if and only if $f\in L\log L(K,μ)$}.\] That is, Wiener's $L\log L$ inequality and its converse inequality due to Stein in 1969 are extended to fractal set $K$ with respect to $μ$.

math.FA↗

arXiv2025

Microcomb-driven large-scale fully connected quantum network

Fully connected quantum networks enable simultaneously connecting every user to every other user and are the most versatile and robust networking architecture. However, the scalability of such networks remains great challenge for practical applications. Here we construct a large-scale fully connected quantum network founded on two-photon Hong-Ou-Mandel (HOM) interference, where user-to-user security is guaranteed even with untrusted network provider. Using integrated soliton microcomb (SMC) and photonic encoding chips, we realize precise massive parallel frequency generation and locking, high-visibility HOM interferences and measurement-device-independent (MDI) quantum key distribution. The proposed architecture enables a 200-user fully connected quantum network over 200 kilometers with strict information-theoretic security via untrusted network provider. The implemented networking architecture paves the way for realizing large-scale fully connected MDI quantum networks across metropolitan and intercity regions.

quant-ph↗

arXiv2025

HyperVL: An Efficient and Dynamic Multimodal Large Language Model for Edge Devices

Current multimodal large lanauge models possess strong perceptual and reasoning capabilities, however high computational and memory requirements make them difficult to deploy directly on on-device environments. While small-parameter models are progressively endowed with strong general capabilities, standard Vision Transformer (ViT) encoders remain a critical bottleneck, suffering from excessive latency and memory consumption when processing high-resolution inputs.To address these challenges, we introduce HyperVL, an efficient multimodal large language model tailored for on-device inference. HyperVL adopts an image-tiling strategy to cap peak memory usage and incorporates two novel techniques: (1) a Visual Resolution Compressor (VRC) that adaptively predicts optimal encoding resolutions to eliminate redundant computation, and (2) Dual Consistency Learning (DCL), which aligns multi-scale ViT encoders within a unified framework, enabling dynamic switching between visual branches under a shared LLM. Extensive experiments demonstrate that HyperVL achieves state-of-the-art performance among models of comparable size across multiple benchmarks. Furthermore, it significantly significantly reduces latency and power consumption on real mobile devices, demonstrating its practicality for on-device multimodal inference.

cs.CV↗

arXiv2025

Taming the Chaos: Coordinated Autoscaling for Heterogeneous and Disaggregated LLM Inference

Serving Large Language Models (LLMs) is a GPU-intensive task where traditional autoscalers fall short, particularly for modern Prefill-Decode (P/D) disaggregated architectures. This architectural shift, while powerful, introduces significant operational challenges, including inefficient use of heterogeneous hardware, network bottlenecks, and critical imbalances between prefill and decode stages. We introduce HeteroScale, a coordinated autoscaling framework that addresses the core challenges of P/D disaggregated serving. HeteroScale combines a topology-aware scheduler that adapts to heterogeneous hardware and network constraints with a novel metric-driven policy derived from the first large-scale empirical study of autoscaling signals in production. By leveraging a single, robust metric to jointly scale prefill and decode pools, HeteroScale maintains architectural balance while ensuring efficient, adaptive resource management. Deployed in a massive production environment on tens of thousands of GPUs, HeteroScale has proven its effectiveness, increasing average GPU utilization by a significant 26.6 percentage points and saving hundreds of thousands of GPU-hours daily, all while upholding stringent service level objectives.

cs.DC↗

arXiv2025

Silicon Micro-Disk Resonator Crossbar Array for High-Speed and High-Density Photonic Convolution Processing

Advanced artificial intelligence (AI) algorithms, particularly those based on artificial neural networks, have garnered significant attention for their potential applications in areas such as image recognition and natural language processing. Notably, neural networks make heavy use of matrix-vector multiplication (MVM) operations, causing substantial computing burden on existing electronic computing systems. Optical computing has attracted considerable attention that can perform optical-domain MVM at an ultra-high speed. In this paper, we introduce a novel silicon photonic micro-disk resonator (MDR) crossbar signal processor designed to support matrix-vector multiplication (MVM) with both high processing speed and enhanced computational density. The key innovation of the proposed MDR crossbar processor is the placement of two MDRs at each crosspoint, enabling simultaneous routing and weighting functions. This design effectively doubles the computational density, improving overall performance. We fabricate a silicon photonic MDR crossbar processor, which is employed to perform convolutional tasks in a convolutional neural network (CNN). The experimental results demonstrate that the photonic processor achieves a classification accuracy of 96% on the MNIST dataset. Additionally, it is capable of scaling to a computational speed of up to 160 tera-operations per second (TOPS) and a computational density as high as 25.6 TOPS/mm2. Our approach holds significant promise for enabling highly efficient, scalable on-chip optical computing, with broad potential applications in AI and beyond.

physics.optics↗

arXiv2025

Chat3GPP: An Open-Source Retrieval-Augmented Generation Framework for 3GPP Documents

The 3rd Generation Partnership Project (3GPP) documents is key standards in global telecommunications, while posing significant challenges for engineers and researchers in the telecommunications field due to the large volume and complexity of their contents as well as the frequent updates. Large language models (LLMs) have shown promise in natural language processing tasks, but their general-purpose nature limits their effectiveness in specific domains like telecommunications. To address this, we propose Chat3GPP, an open-source retrieval-augmented generation (RAG) framework tailored for 3GPP specifications. By combining chunking strategies, hybrid retrieval and efficient indexing methods, Chat3GPP can efficiently retrieve relevant information and generate accurate responses to user queries without requiring domain-specific fine-tuning, which is both flexible and scalable, offering significant potential for adapting to other technical standards beyond 3GPP. We evaluate Chat3GPP on two telecom-specific datasets and demonstrate its superior performance compared to existing methods, showcasing its potential for downstream tasks like protocol generation and code automation.

cs.CL↗