Tootfinder

Noise-Level Diffusion Guidance: Well Begun is Half Done
Harvey Mannering, Zhiwu Huang, Adam Prugel-Bennett
https://arxiv.org/abs/2509.13936 https://arxiv.o…

Noise-Level Diffusion Guidance: Well Begun is Half Done
Diffusion models have achieved state-of-the-art image generation. However, the random Gaussian noise used to start the diffusion process influences the final output, causing variations in image quality and prompt adherence. Existing noise-level optimization approaches generally rely on extra dataset construction, additional networks, or backpropagation-based optimization, limiting their practicality. In this paper, we propose Noise Level Guidance (NLG), a simple, efficient, and general noise-le…

Data-Efficient Quantum Noise Modeling via Machine Learning
Yanjun Ji, Marco Roth, David A. Kreplin, Ilia Polian, Frank K. Wilhelm
https://arxiv.org/abs/2509.12933 https://

Data-Efficient Quantum Noise Modeling via Machine Learning
Maximizing the computational utility of near-term quantum processors requires predictive noise models that inform robust, noise-aware compilation and error mitigation. Conventional models often fail to capture the complex error dynamics of real hardware or require prohibitive characterization overhead. We introduce a data-efficient, machine learning-based framework to construct accurate, parameterized noise models for superconducting quantum processors. Our approach circumvents costly character…

Mitigating the Noise Shift for Denoising Generative Models via Noise Awareness Guidance
Jincheng Zhong, Boyuan Jiang, Xin Tao, Pengfei Wan, Kun Gai, Mingsheng Long
https://arxiv.org/abs/2510.12497

Mitigating the Noise Shift for Denoising Generative Models via Noise Awareness Guidance
Existing denoising generative models rely on solving discretized reverse-time SDEs or ODEs. In this paper, we identify a long-overlooked yet pervasive issue in this family of models: a misalignment between the pre-defined noise level and the actual noise level encoded in intermediate states during sampling. We refer to this misalignment as noise shift. Through empirical analysis, we demonstrate that noise shift is widespread in modern diffusion models and exhibits a systematic bias, leading to …

Shape optimization for trailing-edge noise reduction using large-eddy simulation and ensemble-based method
Qingyong Luo, Xin-Lei Zhang, Guowei He
https://arxiv.org/abs/2509.13752

Shape optimization for trailing-edge noise reduction using large-eddy simulation and ensemble-based method
In this work, the trailing-edge shape of an airfoil is optimized to reduce the acoustic noise based on large-eddy simulation (LES). It is achieved by the ensemble Kalman method, which can enhance the optimization efficiency by using the gradient of cost function approximated with sample covariances. Moreover, the update scheme is reformulated to impose smoothness regularization and enable simultaneous reduction in the trailing edge noise and the drag-to-lift ratio. The trailing edge is optimize…

Novel Phase-Noise-Tolerant Variational-Autoencoder-Based Equalization Suitable for Space-Division-Multiplexed Transmission
Vincent Lauinger, Lennart Schmitz, Patrick Matalla, Andrej Rode, Sebastian Randel, Laurent Schmalen
https://arxiv.org/abs/2509.14072

Novel Phase-Noise-Tolerant Variational-Autoencoder-Based Equalization Suitable for Space-Division-Multiplexed Transmission
We demonstrate the effectiveness of a novel phase-noise-tolerant, variational-autoencoder-based equalization scheme for space-division-multiplexed (SDM) transmission in an experiment over 150km of randomly-coupled multi-core fibers.

ILA: Correctness via Type Checking for Fully Homomorphic Encryption
Tarakaram Gollamudi, Anitha Gollamudi, Joshua Gancher
https://arxiv.org/abs/2509.11559 https://

ILA: Correctness via Type Checking for Fully Homomorphic Encryption
RLWE-based Fully Homomorphic Encryption (FHE) schemes add some small \emph{noise} to the message during encryption. The noise accumulates with each homomorphic operation. When the noise exceeds a critical value, the FHE circuit produces an incorrect output. This makes developing FHE applications quite subtle, as one must closely track the noise to ensure correctness. However, existing libraries and compilers offer limited support to statically track the noise. Additionally, FHE circuits are als…

🇺🇦 #NowPlaying on KEXP's #VarietyMix
WITCH:
🎵 Set Free
#WITCH
https://lotophagusrecords.bandcamp.com/track/noise-witch

Pink noise in Electric Current from Amplitude Modulations
Masahiro Morikawa, Akika Nakamichi
https://arxiv.org/abs/2508.11369 https://arxiv.org/pdf/2508.11…

Pink noise in Electric Current from Amplitude Modulations
We recently proposed that the general origin of 1/f fluctuation, or pink noise, is the amplitude modulation (or beat) of many waves with accumulating frequencies. In this paper, we verify this proposal in the electric current system. We use the classical Langevin equation to describe the electron wave packets flowing in the (semi-)conductor, affected by the back reaction of soft photon emission. If this were amplitude modulation, a demodulation process is needed to extract the fluctuation featu…

Enhancement of Indistinguishable Photon Emission from a GaAs Quantum Dot via Charge Noise Suppression
Priyabrata Mudi, Avijit Barua, Kartik Gaur, Steffen Wilksen, Alexander Steinhoff, Setthanat Wijitpatima, Sarthak Tripathi, Julian Ritzmann, Andreas D. Wieck, Sven Rodt, Christopher Gies, Arne Ludwig, Stephan Reitzenstein
https://

Enhancement of Indistinguishable Photon Emission from a GaAs Quantum Dot via Charge Noise Suppression
The generation of indistinguishable single photons is a fundamental requirement for future quantum technologies, particularly in quantum repeater networks and for distributed quantum computing based on entanglement distribution. However, spectral jitter, often induced by charge noise in epitaxial quantum dots, leads to exciton dephasing, thereby limiting their practical usage in quantum applications. We present a straightforward approach to mitigate charge noise-induced decoherence in droplet-e…

Apple AirPods Pro 3 review: significantly better fit, sound quality, noise cancellation, and battery life, but heart-rate monitoring is buried in fitness apps (David Carnoy/CNET)
https://www.cnet.com/tech/mobile/my-apple-airpods-pro-…

My Apple AirPods Pro 3 Review: Big Improvements, Same Price, No Compromises
They cost the same at a hefty $249, but Apple's third-generation noise-canceling earbuds have improved in all the key areas, including fit, noise cancellation, sound quality and battery life.

Noise Resilience of Spin Quantum Battery in the presence of DM Interactions
Vigneshwar B, Sankaranarayanan R
https://arxiv.org/abs/2508.11333 https://arxiv…

Noise Resilience of Spin Quantum Battery in the presence of DM Interactions
Quantum batteries utilize quantum effects to enhance energy storage and work extraction, offering promising avenues for nanoscale energy applications. However, environmental noise poses a significant challenge by degrading stored energy. For a single qubit, we show that amplitude damping and bit-flip noises lead to ergotropy loss, while phase-flip noise permits partial preservation of work. Extending to a two-qubit Heisenberg XYZ model with Dzyaloshinsky-Moria interaction (DMI), we identify a c…

McKean-Vlasov equations with rough common noise
Peter K. Friz, Antoine Hocquet, Khoa L\^e
https://arxiv.org/abs/2507.13149 https://ar…

McKean-Vlasov equations with rough common noise
We show well-posedness for McKean--Vlasov equations with rough common noise and progressively measurable coefficients. Our results are valid under natural regularity assumptions on the coefficients, in agreement with the respective requirements of Ito and rough path theory. To achieve these goals, we work in the framework of rough stochastic differential equations recently developed by the authors of this article.

AnatoMaskGAN: GNN-Driven Slice Feature Fusion and Noise Augmentation for Medical Semantic Image Synthesis
Zonglin Wu, Yule Xue, Qianxiang Hu, Yaoyao Feng, Yuqi Ma, Shanxiong Chen
https://arxiv.org/abs/2508.11375

AnatoMaskGAN: GNN-Driven Slice Feature Fusion and Noise Augmentation for Medical Semantic Image Synthesis
Medical semantic-mask synthesis boosts data augmentation and analysis, yet most GAN-based approaches still produce one-to-one images and lack spatial consistency in complex scans. To address this, we propose AnatoMaskGAN, a novel synthesis framework that embeds slice-related spatial features to precisely aggregate inter-slice contextual dependencies, introduces diverse image-augmentation strategies, and optimizes deep feature learning to improve performance on complex medical images. Specifical…

Noise Supervised Contrastive Learning and Feature-Perturbed for Anomalous Sound Detection
Shun Huang, Zhihua Fang, Liang He
https://arxiv.org/abs/2509.13853 https://

Noise Supervised Contrastive Learning and Feature-Perturbed for Anomalous Sound Detection
Unsupervised anomalous sound detection aims to detect unknown anomalous sounds by training a model using only normal audio data. Despite advancements in self-supervised methods, the issue of frequent false alarms when handling samples of the same type from different machines remains unresolved. This paper introduces a novel training technique called one-stage supervised contrastive learning (OS-SCL), which significantly addresses this problem by perturbing features in the embedding space and em…

Physics constrained learning of stochastic characteristics
Pardha Sai Krishna Ala, Ameya Salvi, Venkat Krovi, Matthias Schmid
https://arxiv.org/abs/2507.12661

Physics constrained learning of stochastic characteristics
Accurate state estimation requires careful consideration of uncertainty surrounding the process and measurement models; these characteristics are usually not well-known and need an experienced designer to select the covariance matrices. An error in the selection of covariance matrices could impact the accuracy of the estimation algorithm and may sometimes cause the filter to diverge. Identifying noise characteristics has long been a challenging problem due to uncertainty surrounding noise sourc…

Initialization-robust characterization of deep sub-electron read noise pixels via annealed PCH-EM
Aaron J. Hendrickson, David P. Haefner
https://arxiv.org/abs/2509.13460 https:/…

Initialization-robust characterization of deep sub-electron read noise pixels via annealed PCH-EM
We present an annealed Photon Counting Histogram Expectation Maximization (PCH-EM) algorithm for maximum likelihood characterization of Deep Sub-Electron Read Noise (DSERN) pixels. The annealed variant mitigates initialization-dependent convergence to suboptimal local optima of the likelihood function while achieving uncertainties substantially lower than the Photon Transfer (PT) method in the DSERN regime. Although the annealing principle is optimizer-agnostic, we pair it with PCH-EM for tract…

one of my hottest takes is that shared laundry machines in an apartment building is better than in-unit. it's a good example of how individual solutions can be worse than collective ones.
with shared machines you can use two machines at once at a less-busy time. a single in-unit machine would take twice as long. in-unit means the noise the machine generates is inside your apartment, so you can't have guests over or take a meeting while it's running

Music Production: How to Grow a Listener Base with Your Music 📡
https://www.levelsmusicproduction.com/blog/music-production-how-to-grow-a-listener-base-with-your-music?utm_sourc…

Music Production: How to Grow a Listener Base with Your Music
Want to grow a real listener base for your music? Learn how to cut through the noise, connect with fans, and build systems that turn attention into lasting community — with tips, tactics, and a comparison chart.

Stochastic Weakly Convex Optimization Under Heavy-Tailed Noises
Tianxi Zhu, Yi Xu, Xiangyang Ji
https://arxiv.org/abs/2507.13283 https://

Stochastic Weakly Convex Optimization Under Heavy-Tailed Noises
An increasing number of studies have focused on stochastic first-order methods (SFOMs) under heavy-tailed gradient noises, which have been observed in the training of practical deep learning models. In this paper, we focus on two types of gradient noises: one is sub-Weibull noise, and the other is noise under the assumption that it has a bounded $p$-th central moment ($p$-BCM) with $p\in (1, 2]$. The latter is more challenging due to the occurrence of infinite variance when $p\in (1, 2)$. Under…

Black Swan White Noise Podcast
Great Australian Pods Podcast Directory: #GreatAusPods

Atomistic spin dynamics with quantum colored noise
Fried-Conrad Weber, Felix Hartmann, Matias Bargheer, Janet Anders, Richard F. L. Evans
https://arxiv.org/abs/2508.11315 https:…

Atomistic spin dynamics with quantum colored noise
The accurate prediction of temperature-dependent magnetization dynamics is a fundamental challenge in computational magnetism. While Atomistic Spin Dynamics (ASD) simulations have emerged as a powerful tool for studying magnetic phenomena, their classical nature leads to significant deviations from experimental observations, particularly at low temperatures. Here we present a comprehensive implementation of quantum-corrected ASD into the Vampire software package, based on the open-system Landau…

DPI-SPR: A Differentiable Physical Inversion for Shadow Profile Reconstruction Framework in Forward Scatter Radar
ShuQi Lei, Gan Yu, Yuan Tian, XiaoWei Shao
https://arxiv.org/abs/2508.11470

DPI-SPR: A Differentiable Physical Inversion for Shadow Profile Reconstruction Framework in Forward Scatter Radar
Forward scatter radar (FSR) has emerged as an effective imaging modality for target detection, utilizing forward scattering (FS) signals to reconstruct two-dimensional shadow profile images of objects. However, real-world FS signals are inevitably corrupted by noise. Due to the ill-posed nature of electromagnetic inversion and its high sensitivity to noise, existing imaging methods often suffer from degraded performance or even complete failure under low signal-to-noise ratio (SNR) conditions. …

I feel like too often they are used for non-emergency communication and people unsubscribe to avoid the noise

Asymptotic Boundedness of Distributed Set-Membership Filtering
Yudong Li, Yirui Cong, Shimin Wang, Martin Guay, Jiuxiang Dong
https://arxiv.org/abs/2509.14106 https://

Asymptotic Boundedness of Distributed Set-Membership Filtering
Asymptotic boundedness is a crucial property of Distributed Set-Membership Filtering (DSMFing) that prevents the unbounded growth of the set estimates caused by the wrapping effect. However, this important property remains underinvestigated, compared to its noise-free and stochastic-noise counterparts, i.e., the convergence of Distributed Observers (DOs) and the bounded error covariance of Distributed Kalman Filters (DKFs). This paper studies the asymptotic boundedness of DSMFing for linear dis…

Allee Synaptic Plasticity and Memory
Eddy Kwessi
https://arxiv.org/abs/2508.10929 https://arxiv.org/pdf/2508.10929

Allee Synaptic Plasticity and Memory
Neural plasticity is fundamental to memory storage and retrieval in biological systems, yet existing models often fall short in addressing noise sensitivity and unbounded synaptic weight growth. This paper investigates the Allee-based nonlinear plasticity model, emphasizing its biologically inspired weight stabilization mechanisms, enhanced noise robustness, and critical thresholds for synaptic regulation. We analyze its performance in memory retention and pattern retrieval, demonstrating incre…

Random Forest Classification of MBTA Gravitational-Wave Triggers for Low-Latency Detection
Lorenzo Mobilia, Gianluca Maria Guidi
https://arxiv.org/abs/2509.12882 https://…

Random Forest Classification of MBTA Gravitational-Wave Triggers for Low-Latency Detection
In this work, we explore a possible application of a machine learning classifier for candidate events in a template-based search for gravitational-wave (GW) signals from various compact system sources. We analyze data from the O3a and O3b data acquisition campaign, during which the sensitivity of ground-based detectors is limited by real non-Gaussian noise transient. The state-of-the-art searches for such signals tipically rely on the signal-to-noise ratio (SNR) and a chi-square test to assess …

Orbital
Samantha Harvey
A single day in space. A quasi-journal from six character perspectives. Enjoyed the details about space: sleeping, cleaning, noise, routines, huge weather events...
Not sure I'm remember it in x amount of time.
#TheBookerPrize 2024
#SamanthaHarvey

Sporadic Federated Learning Approach in Quantum Environment to Tackle Quantum Noise
Ratun Rahman, Atit Pokharel, Dinh C. Nguyen
https://arxiv.org/abs/2507.12492

Sporadic Federated Learning Approach in Quantum Environment to Tackle Quantum Noise
Quantum Federated Learning (QFL) is an emerging paradigm that combines quantum computing and federated learning (FL) to enable decentralized model training while maintaining data privacy over quantum networks. However, quantum noise remains a significant barrier in QFL, since modern quantum devices experience heterogeneous noise levels due to variances in hardware quality and sensitivity to quantum decoherence, resulting in inadequate training performance. To address this issue, we propose SpoQ…

Improving cosmological reach of a gravitational wave observatory using Deep Loop Shaping
Jonas Buchli, Brendan Tracey, Tomislav Andric, Christopher Wipf, Yu Him Justin Chiu, Matthias Lochbrunner, Craig Donner, Rana X. Adhikari, Jan Harms, Iain Barr, Roland Hafner, Andrea Huber, Abbas Abdolmaleki, Charlie Beattie, Joseph Betzwieser, Serkan Cabi, Jonas Degrave, Yuzhu Dong, Leslie Fritz, Anchal Gupta, Oliver Groth, Sandy Huang, Tamara Norman, Hannah Openshaw, Jameson Rollins, Greg Thornto…

Improving cosmological reach of a gravitational wave observatory using Deep Loop Shaping
Improved low-frequency sensitivity of gravitational wave observatories would unlock study of intermediate-mass black hole mergers, binary black hole eccentricity, and provide early warnings for multi-messenger observations of binary neutron star mergers. Today's mirror stabilization control injects harmful noise, constituting a major obstacle to sensitivity improvements. We eliminated this noise through Deep Loop Shaping, a reinforcement learning method using frequency domain rewards. We proved…

#DropoutTV

The Noise Boys Ride Again - Season 4 - Dropout
Josh, Zac, and Brennan name mobsters and try to hide their bald spots. Content Warning: Comedic Gags Implying Suicide - [2:00-3:13]

Frequency stability of $2.5\times10^{-17}$ in a Si cavity with AlGaAs crystalline mirrors
Dahyeon Lee, Zoey Z. Hu, Ben Lewis, Alexander Aeppli, Kyungtae Kim, Zhibin Yao, Thomas Legero, Daniele Nicolodi, Fritz Riehle, Uwe Sterr, Jun Ye
https://arxiv.org/abs/2509.13503

Frequency stability of $2.5\times10^{-17}$ in a Si cavity with AlGaAs crystalline mirrors
Developments in ultrastable lasers have fueled remarkable advances in optical frequency metrology and quantum science. A key ingredient in further improving laser frequency stability is the use of low-noise mirror materials such as AlGaAs crystalline coatings. However, excess noise observed with these coatings limits the performance of cryogenic silicon cavities with AlGaAs mirrors to similar levels achieved with conventional dielectric coatings. With a new pair of crystalline coated mirrors in…

Andy Reid on chatter that K.C.'s window is closing: 'We don’t listen to all the noise' https://www.nfl.com/news/andy-reid-on-chatter-that-k-c-s-window-is-closing-we-don-t-listen-to-all-the-noise

Phase Transitions of the Additive Uniform Noise Channel with Peak Amplitude and Cost Constraint
Jonas Stapmanns, Catarina Dias, Luke Eilers, Tobias K\"uhn, Jean-Pascal Pfister
https://arxiv.org/abs/2510.12427

Phase Transitions of the Additive Uniform Noise Channel with Peak Amplitude and Cost Constraint
Under which condition is quantization optimal? We address this question in the context of the additive uniform noise channel under peak amplitude and cost constraints. We compute analytically the capacity-achieving input distribution as a function of the noise level, the average cost constraint, and the curvature of the cost function. We find that when the cost function is concave, the capacity-achieving input distribution is discrete, whereas when the cost function is convex and the cost const…

SPG: Style-Prompting Guidance for Style-Specific Content Creation
Qian Liang, Zichong Chen, Yang Zhou, Hui Huang
https://arxiv.org/abs/2508.11476 https://a…

SPG: Style-Prompting Guidance for Style-Specific Content Creation
Although recent text-to-image (T2I) diffusion models excel at aligning generated images with textual prompts, controlling the visual style of the output remains a challenging task. In this work, we propose Style-Prompting Guidance (SPG), a novel sampling strategy for style-specific image generation. SPG constructs a style noise vector and leverages its directional deviation from unconditional noise to guide the diffusion process toward the target style distribution. By integrating SPG with Clas…

A Compositional Kernel Model for Feature Learning
Feng Ruan, Keli Liu, Michael Jordan
https://arxiv.org/abs/2509.14158 https://arxiv.org/pdf/2509.14158

A Compositional Kernel Model for Feature Learning
We study a compositional variant of kernel ridge regression in which the predictor is applied to a coordinate-wise reweighting of the inputs. Formulated as a variational problem, this model provides a simple testbed for feature learning in compositional architectures. From the perspective of variable selection, we show how relevant variables are recovered while noise variables are eliminated. We establish guarantees showing that both global minimizers and stationary points discard noise coordin…

Diffusion is a code repair operator and generator
Mukul Singh, Gust Verbruggen, Vu Le, Sumit Gulwani
https://arxiv.org/abs/2508.11110 https://arxiv.org/pdf…

Diffusion is a code repair operator and generator
Code diffusion models generate code by iteratively removing noise from the latent representation of a code snippet. During later steps of the diffusion process, when the code snippet has almost converged, differences between discrete representations of these snippets look like last-mile repairs applied to broken or incomplete code. We evaluate the extent to which this resemblance can be exploited to leverage pre-trained code diffusion models for the problem of last-mile repair by considering tw…

Simultaneous Calibration of Noise Covariance and Kinematics for State Estimation of Legged Robots via Bi-level Optimization
Denglin Cheng, Jiarong Kang, Xiaobin Xiong
https://arxiv.org/abs/2510.11539

Simultaneous Calibration of Noise Covariance and Kinematics for State Estimation of Legged Robots via Bi-level Optimization
Accurate state estimation is critical for legged and aerial robots operating in dynamic, uncertain environments. A key challenge lies in specifying process and measurement noise covariances, which are typically unknown or manually tuned. In this work, we introduce a bi-level optimization framework that jointly calibrates covariance matrices and kinematic parameters in an estimator-in-the-loop manner. The upper level treats noise covariances and model parameters as optimization variables, while …

Unfolded distillation: very low-cost magic state preparation for biased-noise qubits
Diego Ruiz, J\'er\'emie Guillaud, Christophe Vuillot, Mazyar Mirrahimi
https://arxiv.org/abs/2507.12511

Unfolded distillation: very low-cost magic state preparation for biased-noise qubits
Magic state distillation enables universal fault-tolerant quantum computation by implementing non-Clifford gates via the preparation of high-fidelity magic states. However, it comes at the cost of substantial logical-level overhead in both space and time. In this work, we propose a very low-cost magic state distillation scheme for biased-noise qubits. By leveraging the noise bias, our scheme enables the preparation of a magic state with a logical error rate of $3 \times 10^{-7}$, using only 53 …

$L^2$-solutions to stochastic reaction-diffusion equations with superlinear drifts driven by space-time white noise^
Shijie Shang, Pengyu Wang, Tusheng Zhang
https://arxiv.org/abs/2509.12744

$L^2$-solutions to stochastic reaction-diffusion equations with superlinear drifts driven by space-time white noise^
Consider the following stochastic reaction-diffusion equation with logarithmic superlinear coefficient b, driven by space-time white noise W: $$ u_t(t,x) = (1/2)u_{xx}(t,x) + b(u(t,x)) + σ(u(t,x))W(dt,dx) $$ for $t > 0$ and $x \in [0,1]$, with initial condition $$ u(0,x) = u_0(x) $$ for $x \in [0,1]$, where $u_0 \in L^2[0,1]$. In this paper, we establish existence and uniqueness of probabilistically strong solutions in $C(R_+, L^2[0,1])$. Our result resolves a problem from [Ann. Probab. 47 (…

President of Raider Nation Gives His Take on Upcoming Season https://www.si.com/nfl/raiders/las-vegas-ice-cube-pete-carroll-geno-smith-training-camp

Tunable Random Telegraph Noise in Stable Perpendicular Magnetic Tunnel Junctions for Unconventional Computing
Ahmed Sidi El Valli, Michael Tsao, Dairong Chen, Andrew D. Kent
https://arxiv.org/abs/2509.13458

Tunable Random Telegraph Noise in Stable Perpendicular Magnetic Tunnel Junctions for Unconventional Computing
We demonstrate that thermally stable perpendicular magnetic tunnel junctions (pMTJs), widely used in spin-transfer torque magnetic random-access memory, can be actuated with nanosecond pulses to exhibit tunable stochastic behavior. This actuated-stochastic tunnel junction (A-sMTJ) concept produces random telegraph noise, with control over fluctuation rate and probability bias. The device response is shown to be consistent with a Poisson process, with fluctuation rates tunable over more than two…

Crosslisted article(s) found for cs.HC. https://arxiv.org/list/cs.HC/new
[1/1]:
- Privacy Enhancement for Gaze Data Using a Noise-Infused Autoencoder
Samantha Aziz, Oleg Komogortsev

Emergent structures in open EFTs
Perseas Christodoulidis
https://arxiv.org/abs/2509.13284 https://arxiv.org/pdf/2509.13284…

Emergent structures in open EFTs
Open effective field theories (EFTs) provide a systematic framework for describing systems coupled to an environment, where dissipation, noise, and modified conservation laws naturally arise. Working within the Schwinger-Keldysh formalism, we examine open extensions of well-studied theories describing a spontaneous symmetry breaking phase in the higher-form sense: the superfluid, Maxwell theory, and Einstein's gravity. We demonstrate that to leading order breaking advanced symmetries while pres…

Physics-Informed Neural Networks vs. Physics Models for Non-Invasive Glucose Monitoring: A Comparative Study Under Realistic Synthetic Conditions
Riyaadh Gani
https://arxiv.org/abs/2509.12253

Physics-Informed Neural Networks vs. Physics Models for Non-Invasive Glucose Monitoring: A Comparative Study Under Realistic Synthetic Conditions
Non-invasive glucose monitors often fail outside the lab because existing datasets ignore hardware noise, environmental drift, and person-to-person physiology. We introduce the first ultra-realistic near-infrared (NIR) simulator that injects 12-bit ADC quantisation, +/-0.1% LED ageing, photodiode dark noise, 15-45 C temperature, 30-90% relative humidity, contact-pressure variation, Fitzpatrick I-VI melanin, and diurnal glucose excursions (dawn phenomenon). Using this platform (rho glucose-NIR =…

ENJ: Optimizing Noise with Genetic Algorithms to Jailbreak LSMs
Yibo Zhang, Liang Lin
https://arxiv.org/abs/2509.11128 https://arxiv.org/pdf/2509.11128

ENJ: Optimizing Noise with Genetic Algorithms to Jailbreak LSMs
The widespread application of Large Speech Models (LSMs) has made their security risks increasingly prominent. Traditional speech adversarial attack methods face challenges in balancing effectiveness and stealth. This paper proposes Evolutionary Noise Jailbreak (ENJ), which utilizes a genetic algorithm to transform environmental noise from a passive interference into an actively optimizable attack carrier for jailbreaking LSMs. Through operations such as population initialization, crossover fus…

Metastable transition times of the 1D dynamical sine-Gordon model
Petri Laarne
https://arxiv.org/abs/2509.13806 https://arxiv.org/pdf/2509.13806

Metastable transition times of the 1D dynamical sine-Gordon model
We study the dynamics of a stochastic heat equation with $γ\sin(βu)$ nonlinearity on one-dimensional torus. We show an Eyring--Kramers law for the jump rate between potential wells in the small-noise limit, and that the transition state undergoes a bifurcation at $γβ= 1$. The argument follows the potential-theoretic approach of Berglund and Gentz [Electron. J. Probab. 2013].

A Self-Organized Model for the Flicker Noise in Interacting Two-Dimensional Electron Gas
Maryam Pirgholi, Morteza Nattagh Najafi, Vadood Adami
https://arxiv.org/abs/2509.13090 h…

A Self-Organized Model for the Flicker Noise in Interacting Two-Dimensional Electron Gas
We investigate self-organized criticality in a two-dimensional electron gas (2DEG) by introducing a lattice-based model that incorporates electron-electron interactions through the concept of coherence length. Our numerical simulations demonstrate that in the strongly interacting regime, the system exhibits a distinct set of universal critical exponents, markedly different from those observed in the weakly interacting limit. This dichotomy aligns with experimental findings on the metal-insulato…

Why the noise model matters: A performance gap in learned regularization
Sebastian Banert, Christoph Brauer, Dirk Lorenz, Lionel Tondji
https://arxiv.org/abs/2510.12521 https://…

Why the noise model matters: A performance gap in learned regularization
This article addresses the challenge of learning effective regularizers for linear inverse problems. We analyze and compare several types of learned variational regularization against the theoretical benchmark of the optimal affine reconstruction, i.e. the best possible affine linear map for minimizing the mean squared error. It is known that this optimal reconstruction can be achieved using Tikhonov regularization, but this requires precise knowledge of the noise covariance to properly weight …

Enhancing Electromagnetic Calorimeter Signal Reconstruction with Machine Learning-Based Noise Discrimination
Suman Das Gupta, Shamik Ghosh, Laltu Gazi, Shubham Dutta, Alexander Ledovskoy, Satyaki Bhattacharya, Shilpi Jain
https://arxiv.org/abs/2509.11291

Enhancing Electromagnetic Calorimeter Signal Reconstruction with Machine Learning-Based Noise Discrimination
Calorimeters operating in high-radiation environments are susceptible to damage, leading to increased noise that can significantly degrade energy resolution. A common way to mitigate noise is to apply a higher energy threshold on the cells, typically set a few standard deviations above the noise level. However, this method risks discarding cells with genuine energy deposits, worsening the energy resolution. In this paper we explore various machine learning (ML) algorithms that can replace a rig…

Inverse Physics-informed neural networks procedure for detecting noise in open quantum systems
Gubio G. de Lima, Iann Cunha, Leonardo Kleber Castelano
https://arxiv.org/abs/2507.12552

Inverse Physics-informed neural networks procedure for detecting noise in open quantum systems
Accurate characterization of quantum systems is essential for the development of quantum technologies, particularly in the noisy intermediate-scale quantum (NISQ) era. While traditional methods for Hamiltonian learning and noise characterization often require extensive measurements and scale poorly with system size, machine learning approaches offer promising alternatives. In this work, we extend the inverse physics-informed neural network (referred to as PINNverse) framework to open quantum sy…

Pathwise regularity of solutions for a class of elliptic SPDEs with symmetric L\'evy noise
Juan J. Jim\'enez
https://arxiv.org/abs/2507.12656 https…

Pathwise regularity of solutions for a class of elliptic SPDEs with symmetric Lévy noise
In this article, we investigate the existence and uniqueness of random-field solutions to the elliptic SPDE $-\mathcal{L}u=\dotξ$ on a bounded domain $D$ with Dirichlet boundary conditions $u=0$ on $\partial D$, driven by symmetric Lévy noise $\dotξ$. Under general sufficient conditions on the coefficients of the second-order operator $\mathcal{L}$, we prove the existence of a mild solution via the corresponding Green's function and show that the same framework applies to the spectral fracti…

$\texttt{raccoon}$: A Python package for removing wiggle artifacts in the JWST NIRSpec integral field spectroscopy
Anowar J. Shajib
https://arxiv.org/abs/2507.13341

$\texttt{raccoon}$: A Python package for removing wiggle artifacts in the JWST NIRSpec integral field spectroscopy
$\texttt{raccoon}$ is a Python package for removing resampling noise - commonly referred to as "wiggles'' - from spaxel-level spectra in datacubes obtained from the JWST Near Infrared Spectrograph's (NIRSpec) integral field spectroscopy (IFS) mode. These wiggles arise as artifacts during resampling of the 2D raw data into 3D datacubes, due to the point spread function (PSF) being undersampled. The standard JWST data reduction pipeline does not correct for this noise. The wiggle artifacts can si…

Statistical Properties of Current Noise Induced by Electron-Phonon Scattering in Metallic Carbon Nanotubes
Aina Sumiyoshi, Keisuke Ishizeki, Takahiro Yamamoto
https://arxiv.org/abs/2508.11201

Statistical Properties of Current Noise Induced by Electron-Phonon Scattering in Metallic Carbon Nanotubes
We theoretically investigate current noise in metallic carbon nanotubes induced by electron-phonon scattering, focusing on the probability density function (PDF) of the current that characterizes the nonequilibrium steady state. Quantum transport simulations combined with analyses of higher-order statistical moments reveal that the PDF evolves continuously from a Gaussian distribution in the ballistic regime to a non-Gaussian gamma distribution in the diffusive regime. In the crossover regime, …

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https://lotophagusrecords.bandcamp.com/track/noise-witch

VITA: Vision-to-Action Flow Matching Policy
Dechen Gao, Boqi Zhao, Andrew Lee, Ian Chuang, Hanchu Zhou, Hang Wang, Zhe Zhao, Junshan Zhang, Iman Soltani
https://arxiv.org/abs/2507.13231

VITA: Vision-to-Action Flow Matching Policy
We present VITA, a Vision-To-Action flow matching policy that evolves latent visual representations into latent actions for visuomotor control. Traditional flow matching and diffusion policies sample from standard source distributions (e.g., Gaussian noise) and require additional conditioning mechanisms like cross-attention to condition action generation on visual information, creating time and space overheads. VITA proposes a novel paradigm that treats latent images as the flow source, learnin…

A Deep Multi-Task Learning Approach to Impulsive Noise Parameter Estimation
Abdullahi Mohammad, Bdah Eya, Bassant Selim
https://arxiv.org/abs/2510.12179 https://

A Deep Multi-Task Learning Approach to Impulsive Noise Parameter Estimation
Impulsive noise poses a significant challenge to the reliability of wireless communication systems, necessitating accurate estimation of its statistical parameters for effective mitigation. This paper introduces a multitask learning (MTL) framework based on a CNN-LSTM architecture enhanced with an attention mechanism for the joint estimation of impulsive noise parameters. The proposed model leverages a unified weighted-loss function to enable simultaneous learning of multiple parameters within …

Second-order Optimization under Heavy-Tailed Noise: Hessian Clipping and Sample Complexity Limits
Abdurakhmon Sadiev, Peter Richt\'arik, Ilyas Fatkhullin
https://arxiv.org/abs/2510.10690

Second-order Optimization under Heavy-Tailed Noise: Hessian Clipping and Sample Complexity Limits
Heavy-tailed noise is pervasive in modern machine learning applications, arising from data heterogeneity, outliers, and non-stationary stochastic environments. While second-order methods can significantly accelerate convergence in light-tailed or bounded-noise settings, such algorithms are often brittle and lack guarantees under heavy-tailed noise -- precisely the regimes where robustness is most critical. In this work, we take a first step toward a theoretical understanding of second-order opt…

Adapting Noise to Data: Generative Flows from 1D Processes
Jannis Chemseddine, Gregor Kornhardt, Richard Duong, Gabriele Steidl
https://arxiv.org/abs/2510.12636 https://

Adapting Noise to Data: Generative Flows from 1D Processes
We introduce a general framework for constructing generative models using one-dimensional noising processes. Beyond diffusion processes, we outline examples that demonstrate the flexibility of our approach. Motivated by this, we propose a novel framework in which the 1D processes themselves are learnable, achieved by parameterizing the noise distribution through quantile functions that adapt to the data. Our construction integrates seamlessly with standard objectives, including Flow Matching an…

NAMOUnc: Navigation Among Movable Obstacles with Decision Making on Uncertainty Interval
Kai Zhang, Eric Lucet, Julien Alexandre Dit Sandretto, Shoubin Chen, David Filait
https://arxiv.org/abs/2509.12723

NAMOUnc: Navigation Among Movable Obstacles with Decision Making on Uncertainty Interval
Navigation among movable obstacles (NAMO) is a critical task in robotics, often challenged by real-world uncertainties such as observation noise, model approximations, action failures, and partial observability. Existing solutions frequently assume ideal conditions, leading to suboptimal or risky decisions. This paper introduces NAMOUnc, a novel framework designed to address these uncertainties by integrating them into the decision-making process. We first estimate them and compare the correspo…

Escape-Induced Temporally Correlated Noise Driven Universality Crossover
Mrinal Manna, Sourav Mukherjee, Soumen Giri, Pramod Bhakuni, Sajal Barman, Arnab Kumar Pariari, Anil Gome, Markus Hucker, V. Raghavendra Reddy, Anupam Roy, Sudipta Roy Barman, Smarajit Karmakar, Chandana Mondal, Rajib Batabyal
https://arxiv.org/abs/2510.12593

Escape-Induced Temporally Correlated Noise Driven Universality Crossover
Universal behavior in far-from-equilibrium systems is driven by interactions between transport processes and noise structure. The Kardar-Parisi-Zhang (KPZ) framework predicts that extensions incorporating conserved currents or temporally correlated noise give rise to distinct growth morphologies and universality classes, yet direct experimental realization has remained elusive. Here, we report atomically resolved Sn thin-film growth on Sb-doped MnBi$_2$Te$_4$, revealing a sharp dynamical crosso…

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When Retriever Meets Generator: A Joint Model for Code Comment Generation
Tien P. T. Le, Anh M. T. Bui, Huy N. D. Pham, Alessio Bucaioni, Phuong T. Nguyen
https://arxiv.org/abs/2507.12558

When Retriever Meets Generator: A Joint Model for Code Comment Generation
Automatically generating concise, informative comments for source code can lighten documentation effort and accelerate program comprehension. Retrieval-augmented approaches first fetch code snippets with existing comments and then synthesize a new comment, yet retrieval and generation are typically optimized in isolation, allowing irrelevant neighbors topropagate noise downstream. To tackle the issue, we propose a novel approach named RAGSum with the aim of both effectiveness and efficiency in …

Frequency Dependence of Phonon-Induced Current Noise in ArmchairCarbon Nanotube
Raimu Akimoto, Aina Sumiyoshi, Takahiro Yamamoto
https://arxiv.org/abs/2508.11199 https://…

Frequency Dependence of Phonon-Induced Current Noise in ArmchairCarbon Nanotube
We theoretically investigate the frequency dependence of phonon-induced current noise in armchair carbon nanotubes at room temperature. Our results reveal the emergence of multiple resonance peaks in the high-frequency regime, which cannot be accounted for by the Lorentzian lineshape expected from a Markovian process. The electron-phonon scattering processes responsible for most of these peaks are identified based on energy and momentum conservation laws and conventional selection rules. Howeve…

Readout of a solid state spin ensemble at the projection noise limit
Rouven Maier, Cheng-I Ho, Andrej Denisenko, Marina Davydova, Peter Knittel, J\"org Wrachtrup, Vadim Vorobyov
https://arxiv.org/abs/2509.11854

Readout of a solid state spin ensemble at the projection noise limit
Spin ensembles are central to quantum science, from frequency standards and fundamental physics searches to magnetic resonance spectroscopy and quantum sensing. Their performance is ultimately constrained by spin projection noise, yet solid-state implementations have so far been limited by much larger photon shot noise. Here we demonstrate a direct, quantum non-demolition readout of a mesoscopic ensemble of nitrogen-vacancy (NV) centers in diamond that surpasses the photon shot-noise limit and …

Robust Convolution Neural ODEs via Contractivity-promoting regularization
Muhammad Zakwan, Liang Xu, Giancarlo Ferrari-Trecate
https://arxiv.org/abs/2508.11432 https://

Robust Convolution Neural ODEs via Contractivity-promoting regularization
Neural networks can be fragile to input noise and adversarial attacks. In this work, we consider Convolutional Neural Ordinary Differential Equations (NODEs), a family of continuous-depth neural networks represented by dynamical systems, and propose to use contraction theory to improve their robustness. For a contractive dynamical system two trajectories starting from different initial conditions converge to each other exponentially fast. Contractive Convolutional NODEs can enjoy increase…

Theoretical Detailed Analyses for DC readout and a Fabri-P\'erot gravitational-wave detector
Kouji Nakamura
https://arxiv.org/abs/2508.11098 https://ar…

Theoretical Detailed Analyses for DC readout and a Fabri-Pérot gravitational-wave detector
The quantum expectation value and the stationary noise spectral density for a Fabri-Pérot gravitational-wave detector with a DC readout scheme are discussed in detail only through the quantum electrodynamics of lasers and the Heisenberg equations of mirrors' motion. We demonstrate that the initial conditions of the mirrors' motion concentrate around the fundamental frequency of the pendulum and are not related to the frequency range of our interest. Although in the ideal case, there is a conse…

Robust Beamforming Design for Secure Near-Field ISAC Systems
Ziqiang CHen, Feng Wang, Guojun Han, Xin Wang, Vincent K. N. Lau
https://arxiv.org/abs/2507.12881

Robust Beamforming Design for Secure Near-Field ISAC Systems
This letter investigates the robust beamforming design for a near-field secure integrated sensing and communication (ISAC) system with multiple communication users (CUs) and targets, as well as multiple eavesdroppers. Taking into account the channel uncertainty constraints, we maximize the minimum sensing beampattern gain for targets, subject to the minimum signal-to-interference-plus-noise ratio (SINR) constraint for each CU and the maximum SINR constraint for each eavesdropper, as well as the…

Advancing Real-World Parking Slot Detection with Large-Scale Dataset and Semi-Supervised Baseline
Zhihao Zhang, Chunyu Lin, Lang Nie, Jiyuan Wang, Yao Zhao
https://arxiv.org/abs/2509.13133

Advancing Real-World Parking Slot Detection with Large-Scale Dataset and Semi-Supervised Baseline
As automatic parking systems evolve, the accurate detection of parking slots has become increasingly critical. This study focuses on parking slot detection using surround-view cameras, which offer a comprehensive bird's-eye view of the parking environment. However, the current datasets are limited in scale, and the scenes they contain are seldom disrupted by real-world noise (e.g., light, occlusion, etc.). Moreover, manual data annotation is prone to errors and omissions due to the complexity o…

Differentially Private Decentralized Dataset Synthesis Through Randomized Mixing with Correlated Noise
Utsab Saha, Tanvir Muntakim Tonoy, Hafiz Imtiaz
https://arxiv.org/abs/2509.10385

Differentially Private Decentralized Dataset Synthesis Through Randomized Mixing with Correlated Noise
In this work, we explore differentially private synthetic data generation in a decentralized-data setting by building on the recently proposed Differentially Private Class-Centric Data Aggregation (DP-CDA). DP-CDA synthesizes data in a centralized setting by mixing multiple randomly-selected samples from the same class and injecting carefully calibrated Gaussian noise, ensuring (ε, δ)-differential privacy. When deployed in a decentralized or federated setting, where each client holds only a s…

Numerical Optimization Methods in the environment with Quantum Noise
Tom\'a\v{s} Bezd\v{e}k
https://arxiv.org/abs/2509.13367 https://arxiv.org/pdf/2509…

Numerical Optimization Methods in the environment with Quantum Noise
The accurate calculation of electronic potential energy surfaces for ground and excited states is crucial for understanding photochemical processes, particularly near conical intersections. While classical methods are limited by scaling and quantum algorithms by hardware, this thesis focuses on the State-Averaged Orbital-Optimized Variational Quantum Eigensolver (SA-OO-VQE). This hybrid quantum-classical algorithm provides a balanced description of multiple electronic states by combining quantu…

Joint Bayesian calibration and map-making for intensity mapping experiments
Zheng Zhang, Philip Bull, Mario G. Santos, Ainulnabilah Nasirudin
https://arxiv.org/abs/2509.10992 ht…

Joint Bayesian calibration and map-making for intensity mapping experiments
Line intensity maps have high dynamic range, and demand careful spectral and spatial calibration. We present a Bayesian framework for joint calibration and map-making using Gibbs sampling, which provides access to the full joint posterior of calibration and sky map parameters. Our data model incorporates instrumental noise based on the radiometer equation, capturing the coupling between noise level and system temperature without assuming a fixed noise amplitude. To enable unbiased and fast esti…

Data Denoising and Derivative Estimation for Data-Driven Modeling of Nonlinear Dynamical Systems
Jiaqi Yao, Lewis Mitchell, John Maclean, Hemanth Saratchandran
https://arxiv.org/abs/2509.14219

Data Denoising and Derivative Estimation for Data-Driven Modeling of Nonlinear Dynamical Systems
Data-driven modeling of nonlinear dynamical systems is often hampered by measurement noise. We propose a denoising framework, called Runge-Kutta and Total Variation Based Implicit Neural Representation (RKTV-INR), that represents the state trajectory with an implicit neural representation (INR) fitted directly to noisy observations. Runge-Kutta integration and total variation are imposed as constraints to ensure that the reconstructed state is a trajectory of a dynamical system that remains clo…

Accelerated stochastic first-order method for convex optimization under heavy-tailed noise
Chuan He, Zhaosong Lu
https://arxiv.org/abs/2510.11676 https://a…

Accelerated stochastic first-order method for convex optimization under heavy-tailed noise
We study convex composite optimization problems, where the objective function is given by the sum of a prox-friendly function and a convex function whose subgradients are estimated under heavy-tailed noise. Existing work often employs gradient clipping or normalization techniques in stochastic first-order methods to address heavy-tailed noise. In this paper, we demonstrate that a vanilla stochastic algorithm -- without additional modifications such as clipping or normalization -- can achieve op…

A Closed-form Expression of the Gaussian Noise Model Supporting O-Band Transmission
Zelin Gan, Henrique Buglia, Romulo Aparecido, Mindaugas Jarmolovi\v{c}ius, Eric Sillekens, Jiaqian Yang, Ronit Sohanpal, Robert I. Killey, Polina Bayvel
https://arxiv.org/abs/2510.11867

A Closed-form Expression of the Gaussian Noise Model Supporting O-Band Transmission
We present a novel closed-form model for nonlinear interference (NLI) estimation in low-dispersion O-band transmission systems. The formulation incorporates the four-wave mixing (FWM) efficiency term as well as the coherent contributions of self- and cross-phase modulation (SPM/XPM) across multiple identical spans. This extension enables accurate evaluation of the NLI in scenarios where conventional closed-form Gaussian Noise (GN) models are limited. The proposed model is validated against spli…

Robust and efficient estimation of global quantum properties under realistic noise
Qingyue Zhang, Dayue Qin, Zhou You, Feng Xu, Jens Eisert, You Zhou
https://arxiv.org/abs/2507.13237

Robust and efficient estimation of global quantum properties under realistic noise
Measuring global quantum properties -- such as the fidelity to complex multipartite states -- is both an essential and experimentally challenging task. Classical shadow estimation offers favorable sample complexity, but typically relies on many-qubit circuits that are difficult to realize on current platforms. We propose the robust phase shadow scheme, a measurement framework based on random circuits with controlled-Z as the unique entangling gate type, tailored to architectures such as trapped…

Towards Robust Defense against Customization via Protective Perturbation Resistant to Diffusion-based Purification
Wenkui Yang, Jie Cao, Junxian Duan, Ran He
https://arxiv.org/abs/2509.13922

Towards Robust Defense against Customization via Protective Perturbation Resistant to Diffusion-based Purification
Diffusion models like Stable Diffusion have become prominent in visual synthesis tasks due to their powerful customization capabilities, which also introduce significant security risks, including deepfakes and copyright infringement. In response, a class of methods known as protective perturbation emerged, which mitigates image misuse by injecting imperceptible adversarial noise. However, purification can remove protective perturbations, thereby exposing images again to the risk of malicious fo…

The CCF AATC 2025: Speech Restoration Challenge
Junan Zhang, Mengyao Zhu, Xin Xu, Hui Bu, Zhenhua Ling, Zhizheng Wu
https://arxiv.org/abs/2509.12974 https://

The CCF AATC 2025: Speech Restoration Challenge
Real-world speech communication is often hampered by a variety of distortions that degrade quality and intelligibility. While many speech enhancement algorithms target specific degradations like noise or reverberation, they often fall short in realistic scenarios where multiple distortions co-exist and interact. To spur research in this area, we introduce the Speech Restoration Challenge as part of the China Computer Federation (CCF) Advanced Audio Technology Competition (AATC) 2025. This chall…

A note on Tricomi-type partial differential equations with white noise initial condition
Enrico Bernardi, Alberto Lanconelli
https://arxiv.org/abs/2509.11753 https://

A note on Tricomi-type partial differential equations with white noise initial condition
We study a class of Tricomi-type partial differential equations previously investigated in [28]. Firstly, we generalize the representation formula for the solution obtained there by allowing the coefficient in front of the second-order partial derivative with respect to $x$ to be any non integer power of $t$. Then, we analyze the robustness of that solution by taking the initial data to be Gaussian white noise and we discover that the existence of a well-defined random field solution is lost up…

Pure dephasing increases partition noise in the quantum Hall effect
C. W. J. Beenakker
https://arxiv.org/abs/2509.10242 https://arxiv.org/pdf/2509.10242

Pure dephasing increases partition noise in the quantum Hall effect
Quantum Hall edge channels partition electric charge over N chiral (uni-directional) modes. Intermode scattering leads to partition noise, observed in graphene p-n junctions. While inelastic scattering suppresses this noise by averaging out fluctuations, we show that pure (quasi-elastic) dephasing may enhance the partition noise. The noise power increases by up to 50% for two modes, with a general enhancement factor of 1+1/N in the strong-dephasing limit. This counterintuitive effect is explain…

Queen Detection in Beehives via Environmental Sensor Fusion for Low-Power Edge Computing
Chiara De Luca, Elisa Donati
https://arxiv.org/abs/2509.14061 https://

Queen Detection in Beehives via Environmental Sensor Fusion for Low-Power Edge Computing
Queen bee presence is essential for the health and stability of honeybee colonies, yet current monitoring methods rely on manual inspections that are labor-intensive, disruptive, and impractical for large-scale beekeeping. While recent audio-based approaches have shown promise, they often require high power consumption, complex preprocessing, and are susceptible to ambient noise. To overcome these limitations, we propose a lightweight, multimodal system for queen detection based on environmenta…

Consensus-Based Optimization Beyond Finite-Time Analysis
Pascal Bianchi (IP Paris, S2A), Alexandru-Radu Dragomir (IP Paris, S2A), Victor Priser (IP Paris, S2A)
https://arxiv.org/abs/2509.12907

Consensus-Based Optimization Beyond Finite-Time Analysis
We analyze a zeroth-order particle algorithm for the global optimization of a non-convex function, focusing on a variant of Consensus-Based Optimization (CBO) with small but fixed noise intensity. Unlike most previous studies restricted to finite horizons, we investigate its long-time behavior with fixed parameters. In the mean-field limit, a quantitative Laplace principle shows exponential convergence to a neighborhood of the minimizer x * . For finitely many particles, a block-wise analysis y…

Kalman Filtering of Stationary Graph Signals
Yang Chen, Yeonju Lee, Yao Shi, Qiyu Sun
https://arxiv.org/abs/2509.12605 https://arxiv.org/pdf/2509.12605

Kalman Filtering of Stationary Graph Signals
In this paper, we propose a novel definition of stationary graph signals, formulated with respect to a symmetric graph shift, such as the graph Laplacian. We show that stationary graph signals can be generated by transmitting white noise through polynomial graph channels, and that their stationarity is preserved under polynomial channel transmission. In this paper, we also investigate Kalman filtering to dynamical systems characterized by polynomial state and observation matrices. We demonstr…

Transforming Noise Distributions with Histogram Matching: Towards a Single Denoiser for All
Sheng Fu, Junchao Zhang, Kailun Yang
https://arxiv.org/abs/2510.06757 https://…

Transforming Noise Distributions with Histogram Matching: Towards a Single Denoiser for All
Supervised Gaussian denoisers exhibit limited generalization when confronted with out-of-distribution noise, due to the diverse distributional characteristics of different noise types. To bridge this gap, we propose a histogram matching approach that transforms arbitrary noise towards a target Gaussian distribution with known intensity. Moreover, a mutually reinforcing cycle is established between noise transformation and subsequent denoising. This cycle progressively refines the noise to be co…

Stochastic nonlinear wave equation with rougher than white noise
Xue-Mei Li, Xianfeng Ren
https://arxiv.org/abs/2510.12221 https://arxiv.org/pdf/2510.12221…

Stochastic nonlinear wave equation with rougher than white noise
We study the singular stochastic wave equation on $\mathbb T^2$, with a cubic nonlinearity and Gaussian rough Matérn forcing (a Fourier multiplier of order $α>0$ applied to space-time white noise) and establish local well-posedness for $α< \tfrac{3}{8}$. This extends [GKO18] beyond white noise and strengthens the quadratic-case result [OO21] ($α<\tfrac 12$). Our argument develops new trilinear estimates in Bourgain spaces together with sharp, case-specific cubic counting estimates.

Diagnosing Quantum Circuits: Noise Robustness, Trainability, and Expressibility
Yuguo Shao, Zhengyu Chen, Zhaohui Wei, Zhengwei Liu
https://arxiv.org/abs/2509.11307 https://

Diagnosing Quantum Circuits: Noise Robustness, Trainability, and Expressibility
Parameterized quantum circuits are central to near-term quantum algorithms, and serve as the foundation for many quantum machine learning frameworks, which should be robust to noise, maintain trainability, and exhibit sufficient expressibility. Here we introduce 2MC-OBPPP, a polynomial-time classical estimator that quantifies the three aforementioned diagnostics for a given parameterized quantum circuit. As a demonstration of the power of our approach, we show that moderate amplitude damping no…

Spin-qubit Noise Spectroscopy of Magnetic Berezinskii-Kosterlitz-Thouless Physics
Mark Potts, Shu Zhang
https://arxiv.org/abs/2509.10309 https://arxiv.org/…

Spin-qubit Noise Spectroscopy of Magnetic Berezinskii-Kosterlitz-Thouless Physics
We propose using spin-qubit noise magnetometry to probe dynamical signatures of magnetic Berezinskii-Kosterlitz-Thouless (BKT) physics. For a nitrogen-vacancy (NV) center coupled to two-dimensional XY magnets, we predict distinctive features in the magnetic noise spectral density in the sub-MHz to GHz frequency range. In the quasi-long-range ordered phase, the spectrum exhibits a temperature-dependent power law characteristic of algebraic spin correlations. Above the transition, the noise refle…

Receiver Noise Calibration in CV-QKD accounting for Noise Dynamics
Guillaume Ricard, Yves Jaou\"en, Romain All\'eaume
https://arxiv.org/abs/2509.07549 https://

Receiver Noise Calibration in CV-QKD accounting for Noise Dynamics
Continuous-Variable Quantum Key Distribution (CV-QKD) relies on accurate noise calibration at the receiver to ensure the security of quantum communication. Traditional calibration methods often oversimplify noise characteristics, neglecting the impact of local oscillator (LO) noise and the critical role of noise spectral properties, which can lead to imprecise Shot Noise Calibration (SNC). Our contributions are threefold: 1) we propose an operational framework for calibration, relying on the no…

Quantum Noise Tomography with Physics-Informed Neural Networks
Antonin Sulc
https://arxiv.org/abs/2509.11911 https://arxiv.org/pdf/2509.11911

Quantum Noise Tomography with Physics-Informed Neural Networks
Characterizing the environmental interactions of quantum systems is a critical bottleneck in the development of robust quantum technologies. Traditional tomographic methods are often data-intensive and struggle with scalability. In this work, we introduce a novel framework for performing Lindblad tomography using Physics-Informed Neural Networks (PINNs). By embedding the Lindblad master equation directly into the neural network's loss function, our approach simultaneously learns the quantum sta…

Long-exposure Camera Readout for Coherent Control of Nitrogen-Vacancy Center Spins in Diamond
Jiahui Chen, Qilong Wu, Huihui Yu, Yi-Dan Qu, Yuan Zhang, Xun Yang, Chong-Xin Shan
https://arxiv.org/abs/2509.11723

Long-exposure Camera Readout for Coherent Control of Nitrogen-Vacancy Center Spins in Diamond
Camera-based wide-field quantum noise spectroscopy (QNS) with nitrogen-vacancy (NV) center spins in diamond can be used to extract DC and AC magnetic field noise with sub-micrometer spatial resolution, but its realization is currently limited by the conflict between the optimal optical spin readout time (normally below one microsecond) and the minimal camera exposure time (normally tens of microsecond). In the present article, we demonstrate fully coherent control of NV center spins via Rabi os…

Practical Subarchitectures for Optimal Quantum Layout Synthesis
Kostiantyn V. Milkevych, Jaco van de Pol, Irfansha Shaik
https://arxiv.org/abs/2507.12976 h…

Practical Subarchitectures for Optimal Quantum Layout Synthesis
Quantum Layout Synthesis (QLS) maps a logical quantum circuit to a physical quantum platform. Optimal QLS minimizes circuit size and depth, which is essential to reduce the noise on current quantum platforms. Optimal QLS is an NP-hard problem, so in practice, one maps a quantum circuit to a subset of the complete quantum platform. However, to guarantee optimality, one still has to consider exponentially many subarchitectures. We introduce an effective method to enumerate relevant subarchitect…

Quantum Reinforcement Learning-Guided Diffusion Model for Image Synthesis via Hybrid Quantum-Classical Generative Model Architectures
Chi-Sheng Chen, En-Jui Kuo
https://arxiv.org/abs/2509.14163

Quantum Reinforcement Learning-Guided Diffusion Model for Image Synthesis via Hybrid Quantum-Classical Generative Model Architectures
Diffusion models typically employ static or heuristic classifier-free guidance (CFG) schedules, which often fail to adapt across timesteps and noise conditions. In this work, we introduce a quantum reinforcement learning (QRL) controller that dynamically adjusts CFG at each denoising step. The controller adopts a hybrid quantum--classical actor--critic architecture: a shallow variational quantum circuit (VQC) with ring entanglement generates policy features, which are mapped by a compact multil…

Learning Minimal Representations of Many-Body Physics from Snapshots of a Quantum Simulator
Frederik M{\o}ller, Gabriel Fern\'andez-Fern\'andez, Thomas Schweigler, Paulin de Schoulepnikoff, J\"org Schmiedmayer, Gorka Mu\~noz-Gil
https://arxiv.org/abs/2509.13821

Learning Minimal Representations of Many-Body Physics from Snapshots of a Quantum Simulator
Analog quantum simulators provide access to many-body dynamics beyond the reach of classical computation. However, extracting physical insights from experimental data is often hindered by measurement noise, limited observables, and incomplete knowledge of the underlying microscopic model. Here, we develop a machine learning approach based on a variational autoencoder (VAE) to analyze interference measurements of tunnel-coupled one-dimensional Bose gases, which realize the sine-Gordon quantum fi…

Technical Review on RF-Amplifiers for Quantum Computer Circuits: New Architectures of Josephson Parametric Amplifier
Ahmad Salmanogli, Hesam Zandi, Mahdi Esmaeili, Abolfazl Eskandari, Mohsen Akbari
https://arxiv.org/abs/2507.13187

Technical Review on RF-Amplifiers for Quantum Computer Circuits: New Architectures of Josephson Parametric Amplifier
Josephson Parametric Amplifiers (JPAs) are key components in quantum information processing due to their ability to amplify weak quantum signals with near-quantum-limited noise performance. This is essential for applications such as qubit readout, quantum sensing, and communication, where signal fidelity and coherence preservation are critical. Unlike CMOS and HEMT amplifiers used in conventional RF systems, JPAs are specifically optimized for millikelvin (mK) cryogenic environments. CMOS ampli…

Learning to Restore Heisenberg Limit in Noisy Quantum Sensing via Quantum Digital Twin
Hang Xu, Tailong Xiao, Jingzheng Huang, Jianping Fan, Guihua Zeng
https://arxiv.org/abs/2508.11198

Learning to Restore Heisenberg Limit in Noisy Quantum Sensing via Quantum Digital Twin
Quantum sensors leverage nonclassical resources to achieve sensing precision at the Heisenberg limit, surpassing the standard quantum limit attainable through classical strategies. However, a critical issue is that the environmental noise induces rapid decoherence, fundamentally limiting the realizability of the Heisenberg limit. In this Letter, we propose a quantum digital twin protocol to overcome this issue. The protocol first establishes observable-constrained state reconstruction to infer …

Local nanoscale probing of electron spins using NV centers in diamond
Sergei Trofimov, Christos Thessalonikios, Victor Deinhart, Alexander Spyrantis, Lucas Tsunaki, Kseniia Volkova, Katja H\"oflich, Boris Naydenon
https://arxiv.org/abs/2507.13295

Local nanoscale probing of electron spins using NV centers in diamond
Substitutional nitrogen atoms in a diamond crystal (P1 centers) are, on one hand, a resource for creation of nitrogen-vacancy (NV) centers, that have been widely employed as nanoscale quantum sensors. On the other hand, P1's electron spin is a source of paramagnetic noise that degrades the NV's performance by shortening its coherence time. Accurate quantification of nitrogen concentration is therefore essential for optimizing diamond-based quantum devices. However, bulk characterization methods…

High-performance multiplexed readout of superconducting qubits with a tunable broadband Purcell filter
Yuzhe Xiong, Zilin Wang, Jiawei Zhang, Xuandong Sun, Zihao Zhang, Peisheng Huang, Yongqi Liang, Ji Jiang, Jiawei Qiu, Yuxuan Zhou, Xiayu Linpeng, Wenhui Huang, Jingjing Niu, Youpeng Zhong, Ji Chu, Song Liu, Dapeng Yu
https://arxiv.org/abs…

High-performance multiplexed readout of superconducting qubits with a tunable broadband Purcell filter
Fast, high-fidelity, and low back-action readout plays a crucial role in the advancement of quantum error correction (QEC). Here, we demonstrate high-performance multiplexed readout of superconducting qubits using a tunable broadband Purcell filter, effectively resolving the fundamental trade-off between measurement speed and photon-noise-induced dephasing. By dynamically tuning the filter parameters, we suppress photon-noise-induced dephasing by a factor of 7 in idle status, while enabling rap…

Quantum hierarchical Fokker-Planck equations with U(1) gauge fields: Application to the Aharonov-Bohm ring
Hyeonseok Yang, Shoki Koyanagi, Yoshitaka Tanimura
https://arxiv.org/abs/2509.11462

Quantum hierarchical Fokker-Planck equations with U(1) gauge fields: Application to the Aharonov-Bohm ring
We investigate a three-dimensional subsystem under a time-dependent U(1) gauge field coupled to rotationally invariant environments. To capture the dynamic behavior of the subsystem under thermal excitations and dissipations, it is imperative to treat the bath in a non-Markovian and nonperturbative manner. This is because quantum noise is constrained by the uncertainty principle, which dictates the relationship between the noise correlation time and the amplitude of the energy fluctuation. To t…

The Quantum Control Hierarchy: When Physics-Informed Design Meets Machine Learning
Atta ur Rahman, M. Y. Abd-Rabbou, Cong-feng Qiao
https://arxiv.org/abs/2509.12832 https://

The Quantum Control Hierarchy: When Physics-Informed Design Meets Machine Learning
We address a wide spectrum of quantum control strategies, including various open-loop protocols and advanced adaptive methods. These methodologies apply to few-qubit scenarios and naturally scale to larger N-qubit systems. We benchmark them across fundamental quantum tasks: entanglement preservation/generation, and directed quantum transport in a disordered quantum walk. All simulations are performed in a challenging environment featuring non-Markov colored noise, imperfections, and the Markov …

Universal syndrome-based recovery for noise-adapted quantum error correction
Debjyoti Biswas, Prabha Mandayam
https://arxiv.org/abs/2510.08719 https://arxi…

Universal syndrome-based recovery for noise-adapted quantum error correction
Quantum error correction (QEC) is an essential tool for quantum computing that enables reliable information processing in the presence of noise. Syndrome measurements play a central role in QEC, making it possible to unambiguously identify the location and type of errors. While syndrome extraction is natural for conventional QEC protocols, where the errors satisfy certain algebraic constraints \emph{perfectly}, this feature is largely missing in the framework of approximate or noise-adapted QEC…

Spin-Locking Spectroscopy of Harmonic Motion
Florian Kranzl, Adria Rospars, Johannes Franke, Manoj K. Joshi, Rainer Blatt, Christian F. Roos
https://arxiv.org/abs/2510.08732 htt…

Spin-Locking Spectroscopy of Harmonic Motion
Characterization of noise of a quantum harmonic oscillator is important for many experimental platforms. We experimentally demonstrate motional spin-locking spectroscopy, a method that allows us to directly measure the motional noise spectrum of a quantum harmonic oscillator. We measure motional noise of a single trapped ion in a frequency range from 200 Hz to 5 kHz with a power spectral density that resolves noise over two orders of magnitude. Coherent modulations in the oscillation frequency …