Publications associées à Mesure spectrale

Empirical Sample Complexity of Neural Network Mixed State Reconstruction

Giuseppe Carleo, Filippo Vicentini, Haimeng Zhao

Quantum state reconstruction using Neural Quantum States has been proposed as a viable tool to reduce quantum shot complexity in practical applications, and its advantage over competing techniques has been shown in numerical experiments focusing mainly on ...

Verein Forderung Open Access Publizierens Quantenwissenschaf2024

Dual-frame optimization for informationally complete quantum measurements

Ivano Tavernelli

Randomized measurement protocols such as classical shadows represent powerful resources for quantum technologies, with applications ranging from quantum state characterization and process tomography to machine learning and error mitigation. Recently, the n ...

2024

Non-classical photon-phonon correlations at room temperature

Santiago Tarrago Velez

With the development of quantum optics, photon correlations acquired a prominent role as a tool to test our understanding of physics, and played a key role in verifying the validity of quantum mechanics. The spatial and temporal correlations in a light fie ...

EPFL2021

A projected super-penalty method for the C1-coupling of multi-patch isogeometric Kirchhoff plates

Annalisa Buffa, Luca Coradello

This work focuses on the development of a super-penalty strategy based on the L2-projection of suitable coupling terms to achieve C1-continuity between non-conforming multi-patch isogeometric Kirchhoff plates. In particular, the choice of penalty parameter ...

2021

Sparsely observed functional time series: estimation and prediction

Victor Panaretos, Tomas Rubin

Functional time series analysis, whether based on time or frequency domain methodology, has traditionally been carried out under the assumption of complete observation of the constituent series of curves, assumed stationary. Nevertheless, as is often the c ...

INST MATHEMATICAL STATISTICS2020

Frequency domain theory for functional time series: Variance decomposition and an invariance principle

Neda Mohammadi Jouzdani

This paper is concerned with frequency domain theory for functional time series, which are temporally dependent sequences of functions in a Hilbert space. We consider a variance decomposition, which is more suitable for such a data structure than the varia ...

INT STATISTICAL INST2020

Fourier dimensionality reduction for fast radio transients

Jean-Philippe Thiran, Yves Wiaux, Vijay Kartik, Ming Jiang

In the context of next-generation radio interferometers, we are facing a big challenge of how to economically process data. The classical dimensionality reduction technique, averaging visibilities on time, may dilute fast radio transients (FRT). We propose ...

2019

Two-Color Pump-Probe Measurement of Photonic Quantum Correlations Mediated by a Single Phonon

Vincenzo Savona, Christophe Marcel Georges Galland, Hugo Pierre Alexandre Flayac, Mitchell David Anderson, Kilian Robert Seibold, Santiago Tarrago Velez

We propose and demonstrate a versatile technique to measure the lifetime of the one-phonon Fock state using two-color pump-probe Raman scattering and spectrally resolved, time-correlated photon counting. Following pulsed laser excitation, the n = 1 phonon ...

2018

Functional Decomposition Using Majority

Giovanni De Micheli, Mathias Soeken, Zhufei Chu

Typical operators for the decomposition of Boolean functions in state-of-the-art algorithms are AND, exclusive-OR (XOR), and a 2-to-1 multiplexer (MUX). We propose a logic decomposition algorithm that uses the majority-of-three (MAJ) operation. Such decomp ...

IEEE2018

Detecting and Localizing Differences in Functional Time Series Dynamics: A Case Study in Molecular Biophysics

Victor Panaretos, Shahin Tavakoli

Motivated by the problem of inferring the molecular dynamics of DNA in solution, and linking them with its base-pair composition, we consider the problem of comparing the dynamics of functional time series (FTS), and of localizing any inferred differences ...

American Statistical Association2016