Publications related to Real-time quantum dynamics of thermal states with neural thermofields

Related publications (17)

Deep Neural Networks: Large-Width Behavior and Generalization Bounds

During several decades, neural network architectures have undergone considerable evolution: from shallow to deep, from fully connected to structured (e.g. convolutional, recurrent, or residual), from unnormalized to normalized. The present thesis contribut ...

EPFL2025

Multi-temporal forest monitoring in the Swiss Alps with knowledge-guided deep learning

Devis Tuia, Marc Conrad Russwurm, Thiên-Anh Claris Nguyen, Gaston Jean Lenczner

Monitoring forests, in particular their response to climate and land use change, requires studying long time scales. While efficient deep learning methods have been developed to process short time series of satellite imagery, leveraging long time series of ...

Elsevier Science Inc2024

A square-root speedup for finding the smallest eigenvalue

Thomas Arthur Louis Joseph Guilbaud

We describe a quantum algorithm for finding the smallest eigenvalue of a Hermitian matrix. This algorithm combines quantum phase estimation and quantum amplitude estimation to achieve a quadratic speedup with respect to the best classical algorithm in term ...

2024

Adversarial Training for Jamming-Robust Channel Estimation in OFDM Systems

Pascal Frossard, Javier Alejandro Maroto Morales

Orthogonal frequency-division multiplexing (OFDM) is widely used to mitigate inter-symbol interference (ISI) from multipath fading. However, the open nature of wireless OFDM systems makes them vulnerable to jamming attacks. In this context, pilot jamming i ...

2024

Effective spin-1 breathing kagome Hamiltonian induced by the exchange hierarchy in the maple leaf mineral bluebellite

Pratyay Ghosh

The Heisenberg quantum antiferromagnet on the maple leaf lattice has been shown to feature highly exotic phases, and therefore material realizations are intensely sought after. We determine the magnetic Hamiltonian of the copper mineral bluebellite using d ...

AMER PHYSICAL SOC2024

Statistical mechanics of the maximum-average submatrix problem

Rodrigo Emilio Pérez Ortiz, Lenka Zdeborová, Florent Gérard Krzakala, Vittorio Erba

We study the maximum-average submatrix problem, wherein given an N x N matrix J, one needs to find the k x k submatrix with the largest average number of entries. We investigate the problem for random matrices J, whose entries are i.i.d. random variables, ...

Iop Publishing Ltd2024

Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems

Colin Neil Jones, Melanie Nicole Zeilinger, Roland Schwan, Xuan Truong Nghiem

Physics-informed machine learning (PIML) is a set of methods and tools that systematically integrate machine learning (ML) algorithms with physical constraints and abstract mathematical models developed in scientific and engineering domains. As opposed to ...

IEEE2023

Bayes-optimal Learning of Deep Random Networks of Extensive-width

Lenka Zdeborová, Florent Gérard Krzakala, Hugo Chao Cui

We consider the problem of learning a target function corresponding to a deep, extensive-width, non-linear neural network with random Gaussian weights. We consider the asymptotic limit where the number of samples, the input dimension and the network width ...

2023

Forest Drought Impact Prediction based on Spatio-temporal Satellite Imagery and Weather Forecasts -- A Spatio-Temporal Approach using Convolutional LSTM Models

Devis Tuia

2023

Time-evolution of local information: Thermalization dynamics of local observables

Loïc Jean Pierre Herviou

Quantum many-body dynamics generically result in increasing entanglement that eventually leads to thermalization of local observables. This makes the exact description of the dynamics complex despite the apparent simplicity of (high-temperature) thermal st ...

SCIPOST FOUNDATION2022

Learning of physical systems: from inference to control

Babak Rahmani

To characterize a physical system to behave as desired, either its underlying governing rules must be known a priori or the system itself be accurately measured. The complexity of full measurements of the system scales with its size. When exposed to real-w ...

EPFL2022

Predictive Analysis of Errors During Robot-Mediated Gamified Training

Pierre Dillenbourg, Arzu Güneysu Özgür

This paper presents our approach to predicting future error-related events in a robot-mediated gamified phys- ical training activity for stroke patients. The ability to predict future error under such conditions suggests the existence of distinguishable fe ...

IEEE2022

Predicting chemical hazard across taxa through machine learning

Kristin Schirmer, Julita Maria Stadnicka-Michalak

We applied machine learning methods to predict chemical hazards focusing on fish acute toxicity across taxa. We analyzed the relevance of taxonomy and experimental setup, showing that taking them into account can lead to considerable improvements in the cl ...

PERGAMON-ELSEVIER SCIENCE LTD2022

Operando X-ray diffraction study of thermal and phase evolution during laser powder bed fusion of Al-Sc-Zr elemental powder blends

Helena Van Swygenhoven, Samy Hocine

Elemental powder blends are an emerging alternative to prealloyed powders for high-throughput alloy design via additive manufacturing techniques. Elemental Al+Sc(+Zr) powder blends were processed by laser powder bed fusion into Al-Sc and Al-Sc-Zr alloys, w ...

2022

Image Restoration using Plug-and-Play CNN MAP Denoisers

Sabine Süsstrunk, Siavash Arjomand Bigdeli, David Honzátko

Plug-and-play denoisers can be used to perform generic image restoration tasks independent of the degradation type. These methods build on the fact that the Maximum a Posteriori (MAP) optimization can be solved using smaller sub-problems, including a MAP d ...

SCITEPRESS2020

Theory and Computation of Hall Scattering Factor in Graphene

Samuel Poncé, Feliciano Giustino

The Hall scattering factor, r, is a key quantity for establishing carrier concentration and drift mobility from Hall measurements; in experiments, it is usually assumed to be 1. In this paper, we use a combination of analytical and ab initio modeling to de ...

AMER CHEMICAL SOC2020

Sparse coupled logistic regression to estimate co-activation and modulatory influences of brain regions

Dimitri Nestor Alice Van De Ville, Thomas William Arthur Bolton

Accurate mapping of the functional interactions between remote brain areas with resting-state functional magnetic resonance imaging requires the quantification of their underlying dynamics. In conventional methodological pipelines, a spatial scale of inter ...

IOP PUBLISHING LTD2020