Tootfinder

Quantum Krylov Algorithm for Szeg\"o Quadrature
William Kirby, Yizhi Shen, Daan Camps, Anirban Chowdhury, Katherine Klymko, Roel Van Beeumen
https://arxiv.org/abs/2509.19195

Quantum Krylov Algorithm for Szegö Quadrature
We present a quantum algorithm to evaluate matrix elements of functions of unitary operators. The method is based on calculating quadrature nodes and weights using data collected from a quantum processor. Given a unitary $U$ and quantum states $|ψ_0\rangle$, $|ψ_1\rangle$, the resulting quadrature rules form a functional that can then be used to classically approximate $\langleψ_1|f(U)|ψ_0\rangle$ for any function $f$. In particular, the algorithm calculates Szegö quadr…

from my link log —
Two workers are quadratically better than one: modelling a queue with PRISM.
https://www.hillelwayne.com/post/queueing-prism/
saved 2020-11-02

Estimation and inference in generalised linear models with constrained iteratively-reweighted least squares
Pierre Masselot, Devon Nenon, Jacopo Vanoli, Zaid Chalabi, Antonio Gasparrini
https://arxiv.org/abs/2509.18406

Estimation and inference in generalised linear models with constrained iteratively-reweighted least squares
We propose a simple and flexible framework for generalised linear models (GLM) with linear constraints on the coefficients. Linear constraints are useful in a wide range of applications, allowing the fitting of model with high-dimensional or highly collinear predictors, as well as encoding assumptions on the association between some or all predictors and the response. We propose the constrained iteratively-reweighted least squares (CIRLS) to fit the model, iterating quadratic programs to ensure…

Mit dem neuen #Solarthermiepark Au setzt #Tübingen ein starkes Zeichen für die #Wärmewende.
Auf 23.200 Quadratmetern erzeugt die Anlage jährlich rund 6 Millionen kWh

Sonne ab sofort Partnerin der Tübinger Wärmewende: Stadtwerke weihen Solarthermie-Park Au ein
Drittgrößte Anlage Deutschlands auf 23.000 Quadratmetern | Die Stadtwerke Tübingen (swt) haben jetzt eine der denkbar energiegeladensten Partnerinnen an ihrer Seite: Die Sonne. Sie sorgt ab sofort jährlich für sechs Millionen Kilowattstunden klimafreundliche Fernwärme. Die erzeugte Wärme kann viele Haushalte versorgen, die an das Fernwärmenetz angeschlossen sind. Die erste Solarthermie-Freiflächenanlage der swt ist gleichzeitig ein wichtiger Baustein für die Fernwärmetransformat…

A time-space B-spline integrator for the Burgers' equation
Idris Dag, Serkan U\u{g}urluo\u{g}lu, Nihat Adar
https://arxiv.org/abs/2510.09408 https://ar…

A time-space B-spline integrator for the Burgers' equation
The purpose of this paper is to propose a new algorithm for obtaining approximate solutions to the Burgers' equation (BE). Integration in time by a quadratic B-spline collocation method is shown. To the best of our knowledge, B-splines have not previously been used to integrate partial differential equations in both time and space. First, the BE is integrated using quadratic B-spline functions in time, and then the time-integrated BE is further solved in space via the cubic B-spline collocation…

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.

Composite Generalized Quadratic Noise Modulation via Signal Addition: Towards Higher Dimensional Noise Modulations
Hadi Zayyani, Mohammad Salman, Felipe A. P. de Figueiredo, Rausley A. A. de Souza
https://arxiv.org/abs/2510.01776

Composite Generalized Quadratic Noise Modulation via Signal Addition: Towards Higher Dimensional Noise Modulations
This letter proposes superposing two Generalized Quadratic Noise Modulators (GQNM) by simply adding their outputs. It creates a 16-ary noise modulator that resembles QAM modulators in classical communication. It modulates the information bits on four different means and four different variances. It could also be applied to reach higher-order modulations than 16-ary schemes by adding the outputs of more than two modulators, which is not discussed in detail in this letter and left for future work…

A Comparison of Quadratic and Higher-Order Representations for QAOA
Kristina Bell, Adam Lowe, Emily Coles, Nathanael Ridgway, Gillian Marshall
https://arxiv.org/abs/2509.20127 h…

A Comparison of Quadratic and Higher-Order Representations for QAOA
In this work we consider a routing problem and compare quadratic and higher-order representations using the Quantum Approximate Optimisation Algorithm (QAOA). The majority of works investigating QAOA use quadratic Hamiltonians to represent the considered problems, which can lead to poor scaling in qubit requirements. We address the gap of direct comparisons between quadratic and higher-order forms through an investigation into two distinct formulations of the same use case. We find that the hig…

A Martingale Kernel Two-Sample Test
Anirban Chatterjee, Aaditya Ramdas
https://arxiv.org/abs/2510.11853 https://arxiv.org/pdf/2510.11853

A Martingale Kernel Two-Sample Test
The Maximum Mean Discrepancy (MMD) is a widely used multivariate distance metric for two-sample testing. The standard MMD test statistic has an intractable null distribution typically requiring costly resampling or permutation approaches for calibration. In this work we leverage a martingale interpretation of the estimated squared MMD to propose martingale MMD (mMMD), a quadratic-time statistic which has a limiting standard Gaussian distribution under the null. Moreover we show that the test is…

Monte Carlo quasi-interpolation of spherical data
Zhengjie Sun, Mengyuan Lv, Xingping Sun
https://arxiv.org/abs/2510.12027 https://arxiv.org/pdf/2510.12027…

Monte Carlo quasi-interpolation of spherical data
We establish a deterministic and stochastic spherical quasi-interpolation framework featuring scaled zonal kernels derived from radial basis functions on the ambient Euclidean space. The method incorporates both quasi-Monte Carlo and Monte Carlo quadrature rules to construct easily computable quasi-interpolants, which provide efficient approximation to Sobolev-space functions for both clean and noisy data. To enhance the approximation power and robustness of our quasi-interpolants, we develop a…