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We provide a perfect sampling algorithm for the hard-sphere model on subsets of $\mathbb{R}^d$ with expected running time linear in the volume under the assumption of strong spatial mixing. A large number of perfect and approximate sampling…

Data Structures and Algorithms · Computer Science 2024-08-22 Konrad Anand , Andreas Göbel , Marcus Pappik , Will Perkins

We study the problem of parameter estimation for time-series possessing two, widely separated, characteristic time scales. The aim is to understand situations where it is desirable to fit a homogenized singlescale model to such multiscale…

Statistics Theory · Mathematics 2009-11-11 G. A. Pavliotis , A. M. Stuart

Classical machine learning has succeeded in the prediction of both classical and quantum phases of matter. Notably, kernel methods stand out for their ability to provide interpretable results, relating the learning process with the physical…

Quantum Physics · Physics 2022-05-05 Teresa Sancho-Lorente , Juan Román-Roche , David Zueco

Quantum kernel methods are a proposal for achieving quantum computational advantage in machine learning. They are based on a hybrid classical-quantum computation where a function called the quantum kernel is estimated by a quantum device…

Quantum Physics · Physics 2024-11-13 Ulysse Chabaud , Roohollah Ghobadi , Salman Beigi , Saleh Rahimi-Keshari

The Mixed Lebesgue space is a suitable tool for modelling and measuring signals living in time-space domains. And sampling in such spaces plays an important role for processing high-dimensional time-varying signals. In this paper, we first…

Information Theory · Computer Science 2019-04-02 Yingchun Jiang , Wenchang Sun

We analyse the convergence of sampling algorithms for functions in reproducing kernel Hilbert spaces (RKHS). To this end, we discuss approximation properties of kernel regression under minimalistic assumptions on both the kernel and the…

Machine Learning · Statistics 2025-04-21 Armin Iske

A canonical transformation is performed on the phase space of a number of homogeneous cosmologies to simplify the form of the scalar (or, Hamiltonian) constraint. Using the new canonical coordinates, it is then easy to obtain explicit…

General Relativity and Quantum Cosmology · Physics 2009-07-10 Abhay Ashtekar , Ranjeet S. Tate , Claes Uggla

The use of quantum resources can provide measurement precision beyond the shot-noise limit (SNL). The task of ab initio optical phase measurement---the estimation of a completely unknown phase---has been experimentally demonstrated with…

This work investigates the coexistence of distinct topologically ordered phases within a single setup. We demonstrate this concept through tensor network simulations of the Hofstadter-Bose-Hubbard model under a spatially modulated chemical…

Quantum metrology aims to exploit quantum phenomena to overcome classical limitations in the estimation of relevant parameters. We consider a probe undergoing a phase shift $\varphi$ whose generator is randomly sampled according to a…

Quantum Physics · Physics 2017-06-07 Rozhin Yousefjani , Rosanna Nichols , Shahriar Salimi , Gerardo Adesso

The representation of quantum states via phase-space functions constitutes an intuitive technique to characterize light. However, the reconstruction of such distributions is challenging as it demands specific types of detectors and detailed…

Resonances are common in wave physics and their full and rigorous characterization is crucial to correctly tailor the response of a system in both time and frequency domains. However, they have been conventionally described by the quality…

Optics · Physics 2025-04-09 Isam Ben Soltane , Nicolas Bonod

We study some connections between the random moment problem and the random matrix theory. A uniform draw in a space of moments can be lifted into the spectral probability measure of the pair (A,e) where A is a random matrix from a classical…

Probability · Mathematics 2009-09-29 Fabrice Gamboa , Alain Rouault

Phase estimation protocols provide a fundamental benchmark for the field of quantum metrology. The latter represents one of the most relevant applications of quantum theory, potentially enabling the capability of measuring unknown physical…

Boson Sampling is the problem of sampling from the same distribution as indistinguishable single photons at the output of a linear optical interferometer. It is an example of a non-universal quantum computation which is believed to be…

Quantum Physics · Physics 2019-11-28 Alexandra E. Moylett , Raúl García-Patrón , Jelmer J. Renema , Peter S. Turner

After a derivation of the quantum Bayes theorem, and a discussion of the reconstruction of the unknown state of identical spin systems by repeated measurements, the main part of this paper treats the problem of determining the unknown phase…

Quantum Physics · Physics 2009-11-11 Filippo Neri

We present a reformulation of quantum mechanics in terms of probability measures and functions on a general classical sample space and in particular in terms of probability densities and functions on phase space. The basis of our proceeding…

Quantum Physics · Physics 2007-05-23 Werner Stulpe

We employ the familiar canonical quantization procedure in a given cosmological setting to argue that it is equivalent to and results in the same physical picture if one considers the deformation of the phase-space instead. To show this we…

General Relativity and Quantum Cosmology · Physics 2010-05-25 N. Khosravi , H. R. Sepangi , B. Vakili

The measurement problem for the optical phase has been traditionally attacked for noiseless schemes or in the presence of amplitude or detection noise. Here we address estimation of phase in the presence of phase diffusion and evaluate the…

Quantum Physics · Physics 2015-03-17 Marco G. Genoni , Stefano Olivares , Matteo G. A. Paris

We deal with the comparison of space-time covariance kernels having, either, full, spatially dynamical, or space-time compact support. Such a comparison is based on compatibility of these covariance models under fixed domain asymptotics,…

Statistics Theory · Mathematics 2023-06-13 Tarik Faouzi , Reinhard Furrer , Emilio Porcu