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Accelerated algorithms have broad applications in large-scale optimization, due to their generality and fast convergence. However, their stability in the practical setting of noise-corrupted gradient oracles is not well-understood. This…

Optimization and Control · Mathematics 2018-08-01 Michael B. Cohen , Jelena Diakonikolas , Lorenzo Orecchia

This paper addresses the estimation of uncertain distributed diffusion coefficients in elliptic systems based on noisy measurements of the model output. We formulate the parameter identification problem as an infinite dimensional…

Optimization and Control · Mathematics 2015-06-11 Jeff Borggaard , Hans-Werner van Wyk

We present a new method to search for long transient gravitational waves signals, like those expected from fast spinning newborn magnetars, in interferometric detector data. Standard search techniques are computationally unfeasible (matched…

The gravitational coupling of nearby massive bodies to test masses in a gravitational wave (GW) detector cannot be shielded, and gives rise to 'gravity gradient noise' (GGN) in the detector. In this paper we show that for any GW detector…

General Relativity and Quantum Cosmology · Physics 2021-05-24 Michael A. Fedderke , Peter W. Graham , Surjeet Rajendran

Satellite geodesy uses the measurement of the motion of one or more satellites to infer precise information about the Earth's gravitational field. In this work, we consider the achievable precision limits on such measurements by examining…

We present an estimation of the noise induced by scattered light inside the main arms of the Einstein Telescope (ET) gravitational wave detector. Both ET configurations for high- and low-frequency interferometers are considered, for which…

General Relativity and Quantum Cosmology · Physics 2023-07-27 M. Andrés-Carcasona , A. Macquet , M. Martínez , Ll. M. Mir , H. Yamamoto

The probabilistic surrogates used by Bayesian optimizers make them popular methods when function evaluations are noisy or expensive to evaluate. While Bayesian optimizers are traditionally used for global optimization, their benefits are…

Optimization and Control · Mathematics 2026-05-14 André L. Marchildon , David W. Zingg

Understanding and mitigating noise in quantum systems is a fundamental challenge in achieving scalable and fault-tolerant quantum computation. Error modeling for quantum systems can be formulated in many ways, some of which are very…

Correlated noise sources, particularly magnetic noise, form a risk to future gravitational-wave searches aimed at detecting the gravitational-wave background. Potential noise contamination is investigated by making noise projections which…

A compact detector for space-time metric and curvature is highly desirable. Here we show that quantum spatial superpositions of mesoscopic objects, of the type which would in principle become possible with a combination of state of the art…

General Relativity and Quantum Cosmology · Physics 2020-08-26 Ryan J. Marshman , Anupam Mazumdar , Gavin W. Morley , Peter F. Barker , Steven Hoekstra , Sougato Bose

The presence of noise is the primary challenge in realizing fault-tolerant quantum computers. In this work, we introduce and experimentally validate a novel strategy to circumvent noise by exploiting the phenomenon of metastability, where a…

Quantum Physics · Physics 2026-05-28 Antonio Sannia , Pratik Sathe , Luis Pedro García-Pintos

The Hessian-vector product has been utilized to find a second-order stationary solution with strong complexity guarantee (e.g., almost linear time complexity in the problem's dimensionality). In this paper, we propose to further reduce the…

Optimization and Control · Mathematics 2017-10-03 Mingrui Liu , Tianbao Yang

Natural Gradient Descent, a second-degree optimization method motivated by the information geometry, makes use of the Fisher Information Matrix instead of the Hessian which is typically used. However, in many cases, the Fisher Information…

Machine Learning · Computer Science 2023-03-10 Rajesh Shrestha

We present a noise estimation and subtraction algorithm capable of increasing the sensitivity of heterodyne laser interferometers by one order of magnitude. The heterodyne interferometer is specially designed for dynamic measurements of a…

Instrumentation and Detectors · Physics 2022-09-13 Yanqi Zhang , Adam Hines , Guillermo Valdes , Felipe Guzman

The Newtonian constant of gravitation $G$ historically has the largest relative uncertainty over all other fundamental constants with some discrepancies in values between different measurements. We propose a new scheme to measure $G$ by…

Instrumentation and Detectors · Physics 2020-03-02 Akio Kawasaki

Recent technological developments have focused the interest of the quantum computing community on investigating how near-term devices could outperform classical computers for practical applications. A central question that remains open is…

Quantum Physics · Physics 2021-11-24 Daniel Stilck Franca , Raul Garcia-Patron

The gradient noise of SGD is considered to play a central role in the observed strong generalization abilities of deep learning. While past studies confirm that the magnitude and the covariance structure of gradient noise are critical for…

Machine Learning · Computer Science 2020-06-22 Jingfeng Wu , Wenqing Hu , Haoyi Xiong , Jun Huan , Vladimir Braverman , Zhanxing Zhu

The effect of noise induced by gravitons has been investigated using a Bose-Einstein condensate. The gravitational wave perturbation is then considerd as a sum of discrete Fourier modes in the momentum space. Coming to an operatorial…

High Energy Physics - Theory · Physics 2024-07-17 Soham Sen , Sunandan Gangopadhyay

This work for the first time addresses the feasibility of measuring millihertz gravitational waves (mHz GWs) with a storage ring-based detector. While this overall challenge consists of several partial problems, here we focus solely on…

General Relativity and Quantum Cosmology · Physics 2024-10-11 Thorben Schmirander , Velizar Miltchev , Suvrat Rao , Marcus Brüggen , Florian Grüner , Wolfgang Hillert , Jochen Liske

In view of solving convex optimization problems with noisy gradient input, we analyze the asymptotic behavior of gradient-like flows under stochastic disturbances. Specifically, we focus on the widely studied class of mirror descent schemes…

Optimization and Control · Mathematics 2017-09-21 Panayotis Mertikopoulos , Mathias Staudigl
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